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Learning to Do Qualitative Data Analysis: A Starting Point

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Quantitative and Qualitative Research: An Overview of Approaches

• First Online: 03 January 2022

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• Euclid Seeram 5 , 6 , 7  

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In Chap. 1 , the nature and scope of research were outlined and included an overview of quantitative and qualitative research and a brief description of research designs. In this chapter, both quantitative and qualitative research will be described in a little more detail with respect to essential features and characteristics. Furthermore, the research designs used in each of these approaches will be reviewed. Finally, this chapter will conclude with examples of published quantitative and qualitative research in medical imaging and radiation therapy.

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Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach

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Seeram, E. (2021). Quantitative and Qualitative Research: An Overview of Approaches. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_2

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Research plays a very important role in making sense of the world around us and developing knowledge basis and systems. As such, understanding research methods and paradigms is very important to scholars and researchers if they are to come up with credible and comprehensive research. This paper discusses the differences between qualitative and quantitative research methods and also looks at how the two methods can be similar and how they can be used together. Qualitative research is a scientific and systematic method used to gather data that it not quantifiable (Yin 2018). This type of research, as Marshall (2016)) explains, "refers to the meanings, concepts definitions, characteristics, metaphors, symbols, and description of things". Therefore, as a research method, qualitative research is also used to unearth new trends in thought processes and actions, how people feel about specific circumstances and to get to the heart of issues and how they affect people (Wolcott 2016). Marshall (2016) emphasizes that qualitative research is primarily exploratory research and is used to obtain information such as intentions and motives that helps explain an occurrence. Thus, qualitative research methods help to understand new occurrences or trends and then helps to explain why such things are happening or occurring. Quantitative Research is used to quantify the problem by way of generating numerical data or data that can be transformed into useable statistics (Lichtman 2017). According to Wolcott (2016) quantitative research is used to quantify attitudes, opinions, behaviors, and other defined variables and generalize results from a larger sample population. Thus, quantitative Research uses measurable data to formulate facts and uncover patterns in research to make sense or deductions on how things have unfolded. Therefore, where qualitative data seeks to understand a phenomenon, quantitative methods seek to quantify them and identify variables that can be measured. Qualitative research uses data collecting methods that often require the direct participation of the researcher to gather data and information crucial to the study. Morgan (2017) notes that

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Mixed methods research explained: Combine data like a pro

From heatmaps to interviews, here’s how to blend qualitative and quantitative data for holistic user insights.

Ella Webber

Mixed methods research is one of the most popular and powerful UX research approaches—blending numbers with narrative to garner a holistic understanding of your product or research question.

Whether you’re in UX research and design, education, healthcare, or social sciences, mixed methods research can help you find insights and make better decisions.

Read on for a breakdown of what mixed methods are, their strengths and weaknesses, when to use them, and how to analyze the data.

UX research made easy

Explore the power of combining quantitative and qualitative research to discover new insights and test final solutions.

What is mixed methods research and when should you use it?

Mixed methods research involves collecting, analyzing, and integrating both quantitative and qualitative UX research methods within a single study. It is unique to other UX research techniques in that it combines data types, encouraging product teams to use qualitative feedback to explain the story behind quantitative numbers.

• Quantitative data can come from UX surveys , product analytics , usability testing , experiments, or statistical databases and provide broad numerical insights
• Qualitative data is gathered through user interviews , focus groups, or contextual inquiries and offers a deep, contextual understanding of the subject matter

Why use a mixed methods approach?

The power of mixed methods research is simple: it allows you to combine the best parts of both types of data—quantitative research methods, like surveys, give you broad trends, while qualitative methods, such as interviews, dig deep into personal experiences.

Anthony J. Onwuegbuzie and R. Burke Johnson, in Mixed Methods Research: A Research Paradigm Whose Time Has Come , highlight how blending these methods allows researchers to leverage the strengths of both approaches. They identify mixed methods research as one of the “three core research paradigms: qualitative, quantitative, and mixed methods.”

Like any technique, however, mixed methods research has both strengths and weaknesses to consider.

When should you use mixed methods research?

Mixed UX research methods are useful when neither qualitative nor quantitative data alone can fully answer your research question . Evaluative research further helps to assess the effectiveness of your mixed method research findings and ensure they meet user needs.

For example, use mixed methods research when:

• You need to go beyond numbers (generalizability): Quantitative methods, like surveys, provide broad trends and patterns that are relevant to a wider population. For example, a survey might show that most users enjoy a new app feature, but it won’t capture why some users might dislike it.
• The why matters (contextualization): Mixed methods allow you to put numerical findings in context, adding rich detail to your conclusions. For example, if analytics show that users are spending less time on your app (quantitative), interviews can help you understand the reasons behind this behavior, such as frustration with a recent update or a lack of engaging content (qualitative).
• Credibility is important (credibility and triangulation): When both data types converge on the same conclusion, it strengthens your findings. For example, the combined evidence is more credible if survey data indicates that most users prefer a particular software interface and focus groups echo this preference.
• You need to track changes (developmental purposes): Mixed methods are invaluable when one type of data informs the other. For example, initial qualitative research with a small group of beta testers can uncover key issues and user needs, which can then be explored quantitatively with a larger user base to see how widespread these issues are.
• Understand complex issues (complementary insights): Different data types can offer complementary insights. For example, in a study on software usability, quantitative data might show a drop in task completion rates, while qualitative data reveals specific pain points and user frustrations. This combined approach can guide more effective design improvements.

What are the types of mixed methods research design?

The type of mixed methods research design you choose depends on your research goals, the timing of data collection, and each data type. Here are some key factors to consider:

• Your research approach: Are you trying to understand existing findings (explanatory) or dig deeper into a topic (exploratory)?
• Your research questions: Do your questions need big-picture answers (like how many users are happy) and detailed explanations (like why some users are unhappy)?
• Existing data availability: Is there any existing information you can use from previous studies or a research repository (like user demographics)?
• Data you can collect yourself: What kind of in-depth information do you need to gather from users (through interviews, testing, etc.)?

Whether you're a data diver or a narrative novelist, understanding these research methods can make your studies more dynamic and insightful.

📚 A UX research repository is crucial for keeping track of research findings. You need a centralized database to store and manage all your qualitative and quantitative data. This ensures that your research is organized, accessible, and reusable for future studies.

Let’s look at the most common types of mixed methods research design:

Convergent parallel

Convergent parallel design involves collecting qualitative and quantitative data simultaneously but analyzing them separately. The primary goal is to merge the two datasets to provide a complete understanding of the research problem.

For example, let’s say you want to study user satisfaction with a new mobile app. Here’s how you might use the convergent parallel design:

• Qualitative results: Conduct in-depth user interviews with 30 participants to gather detailed insights into their experiences and perceptions of the app. Plus, analyze 200 user reviews from app stores. You might use prompts like, "What features do you find most valuable?" and "Please describe any difficulties you've experienced while using the app."
• Quantitative study: Use analytics data to measure user engagement metrics like session duration and feature usage, then distribute UX surveys to gather quantitative satisfaction scores.

Concurrent embedded design

Embedded design is a mixed methods research approach where qualitative and quantitative data are collected simultaneously, but one type of data is supplementary to the other.

The secondary data provides additional context and can help explain or clarify the primary findings. This approach is particularly beneficial when time or resources are limited, as it allows for a more comprehensive analysis without doubling the workload.

Explanatory sequential design

Explanatory sequential design is a popular mixed methods research approach introduced by John W. Creswell and Vicki L. Plano Clark. This research design involves collecting and analyzing quantitative data first, followed by qualitative data collection and analysis.

According to Creswell, this approach is particularly useful when researchers need to explain relationships found in quantitative data.

The process typically involves two phases:

• Quantitative phase: This involves collecting numerical data through methods like surveys or experiments. The goal here is to identify patterns, trends, or relationships.
• Qualitative phase: Qualitative phase: After analyzing the quantitative data, researchers collect qualitative data with qualitative approaches, like interviews or focus groups, to provide deeper insights. This phase helps explain the ‘why’ or ‘how’ behind the quantitative findings.

Creswell emphasizes that one of the strengths of this design is its simple structure, making it easy for researchers to manage and for audiences to understand the research process and findings.

Exploratory sequential design

Exploratory sequential design begins with qualitative data collection and analysis, followed by quantitative data collection. This immersive approach helps generate rich, detailed data that lays a strong foundation for the subsequent quantitative analysis.

For example, let’s say a researcher wants to understand why people don't meditate regularly. They could start with generative research techniques , like conducting workshops where participants discuss their daily routines and barriers to meditation. These qualitative insights reveal underlying themes and patterns, like time constraints and lack of motivation.

Next, the researcher analyzes these qualitative data to identify key factors impacting wellbeing habits. Based on these insights, they develop a survey to quantitatively measure how widespread these barriers are among a larger population.

So, that’s how you collect data. But how do you analyze it? Unsurprisingly, there are multiple analysis and interpretation methods commonly used in mixed methods research. Let’s look at some.

How to analyze mixed methods research data: 3 Ways to combine qualitative and quantitative data

Combining different types of research data can add credibility to your research findings. Let’s look at how to conduct mixed methods research:

Triangulation protocol

Following a thread, mixed methods matrix.

The triangulation protocol in mixed methods research is a systematic way to use multiple data sources, techniques, or perspectives to get a clear understanding of a research problem. The goal is to capitalize on the strengths of both types of data while minimizing their individual weaknesses.

Let's say you want to conduct a study aiming to evaluate the effectiveness of a new educational program on student performance, and you arrive at the following datasets:

• Quantitative finding: 80% of students improved their math scores after the program
• Qualitative finding: Students reported that interactive activities helped them understand math concepts better

When you merge these findings, the research concludes that the interactive activities (identified qualitatively) are likely a significant factor contributing to the improved scores (quantitatively).

The following a thread method allows researchers to trace a specific theme or concept across both qualitative and quantitative data sets.

Here’s how it works:

• Identify key themes: Begin by identifying key themes or variables that are central to your research questions. These themes will serve as the ‘threads’ you’ll follow through your data.
• Extracting data: Extract relevant data segments related to each theme from qualitative (e.g. interviews, focus groups) and quantitative (e.g. surveys, statistical data) sources. This involves coding qualitative data and identifying relevant quantitative measures.
• Mapping data: Create a map or matrix that links data segments from different sources according to the identified themes. This matrix helps visualize how different data points converge or diverge on the same theme.
• Comparative analysis: Compare the data segments within each theme to identify patterns, consistencies, and discrepancies. Look for how qualitative narratives support or contradict quantitative findings.
• Synthesis and interpretation: Synthesize the findings to develop an understanding of each theme. Interpret the data by integrating the qualitative insights with the quantitative results, explaining how they complement or contrast with each other.

A mixed methods matrix is a visual tool used to integrate and compare qualitative and quantitative data in mixed methods research. It helps researchers organize data according to key themes or variables, facilitating a comprehensive analysis and interpretation.

The matrix consists of several rows and columns:

• Rows represent key themes or research questions
• Columns represent different data sources or methods (e.g. interviews, surveys, observations)

By populating each cell with relevant data segments, researchers can easily identify areas of convergence, divergence, and complementarity. Let’s say you want to answer this research question: How does a new health intervention impact patient satisfaction and health outcomes?

You would populate the matrix as follows:

How to conduct mixed methods research: A mixed method research example

Let’s say you own a project management app and want to understand user satisfaction and identify areas for improvement. Here are eight steps to apply mixed methods research—using the convergent parallel technique—to discover user pain points and create a better user experience.

Step 1: Define your research objectives

In UX research , asking the right questions is crucial for identifying user needs and pain points effectively. But in order to write the right user research questions , you need to define a clear objective. What are you looking to understand?

Defining a clear UX research objective helps guide all other research decisions and acts as a lighthouse that guides your research project.

In our example , our research objective could be ‘to explore user experience and identify areas for improvement within our project management app’.

Step 2: Design your study and recruit participants

Ensure your study is designed to allow integration of both quantitative and qualitative data. There are various mixed method research designs to choose from—the right one for you depends on your research objectives and preferences.

At this stage, you should also establish a clear strategy for data integration and decide how you’ll combine the qualitative and quantitative data during the UX reporting and analysis phase. This might involve merging data sets for comparative analysis , or embedding one data set within the other to provide additional context.

The integration plan should reflect your research goals and ensure that the combined data offers a clear understanding. For our study, we’ll design a convergent parallel mixed methods study and triangulate our data during the analysis phase. This enables us to find our what and our why.

This is also when you need to recruit research participants for your study. Consider what you’re studying and identify your target test audience. You then need to create a call-out for your research study—either on socials, via email, or with In-Product Prompts .

Alternatively, you can find and filter research participants using Maze Panel , then manage your participant relationships using Maze Reach .

Step 3: Collect quantitative data

Next up, you want to start gathering your quantitative data. A good way to do this is with a survey to collect numerical data that can be statistically analyzed. For example, a user satisfaction survey that includes rating scales (1–10) for various aspects of the software.

For our research into app user satisfaction, we asked:

• Please rate your overall satisfaction with the app (1–10)
• How often do you use the app per week?
• How easy is the app to use on a scale of 1 to 10?
• How likely are you to recommend the app to a friend or colleague (1–10)?

❓ Need a quick and easy way to create and manage surveys? Maze Feedback Surveys simplify your feedback collection process so you can focus on making the changes your customers want to see. You can quickly create surveys tailored to your needs with Maze's survey templates .

Step 4: Collect qualitative data

Once you’ve got your quantitative data, it’s time to collect your qualitative data. Consider conducting user interviews or focus groups to obtain detailed, descriptive data that provides context and deep understanding.

For our study, we selected 20 users from the survey who gave varied ratings and conducted 30-minute interviews, asking:

• What do you like most about the app?
• What features do you find difficult to use?
• Can you describe a recent experience using the app?
• What improvements would you suggest?

💬 User interviews are resource-intensive and time-consuming. Speed them up with Maze’s end-to-end user interview solution: Interview Studies .

Step 5: Quantitative data analysis

Now you’ve got all your data—it’s time to dig in. For your quantitative data, this involves using statistical methodology to identify trends and patterns.

When we looked at our example data, we calculated:

• CSAT score: 75%
• Frequency of use: 70% use the app daily
• Ease of use average score: 6.8/10
• Net Promoter Score (NPS): 65

Step 6: Qualitative data analysis

Analyzing qualitative data involves coding and categorizing qualitative responses to uncover themes and patterns. Identify recurring themes in user feedback, such as ease of use, functionality, and improvement areas. If you’re using Maze Interview Studies to analyze your findings, you can automatically extract key themes and summaries to speed this process up.

When reviewing qualitative data, we found a number of interesting nuggets in our qualitative data:

• Users express dissatisfaction with the app’s usability, specifically the navigation between different functionalities
• Users wish they could access their billing details via the app, instead of solely via the web
• User find the core functionality—the project management features—to be highly valuable to their day-to-day, but also report finding the interface to be clunky and unintuitive

Step 7: Integrate data and interpret findings

Following your analysis, combine the findings from both data sets and draw conclusions. Look for correlations and insights that span both types of data.

Example integration:

• High satisfaction scores (75%) but lower ease of use (6.8/10) prove a strong product market fit but call for a more intuitive experience
• Further qualitative research agreed with this conclusion and identified specific areas for improvement, such as adding additional functionalities and improving the interface

Step 8: Report findings to stakeholders for buy-in

Present the integrated results to highlight how qualitative insights support or explain quantitative trends.

The format of your report will depend on your audience:

• Internal stakeholders (project managers, designers): Consider a concise report with clear visuals like charts, graphs, and user quotes to highlight key findings and actionable recommendations
• External stakeholders (clients, investors): Create a formal report with a clear introduction, methodology section, and comprehensive results presentation, summarizing key findings and highlighting the impact on user satisfaction and app usage

Always strive to go beyond what the data says and explain why it matters.

For example, once we’d conducted our research and drawn conclusions, we compiled this into a report that shared:

• Research methods: We used mixed methods research (surveys and interviews) to explore existing user pain points and satisfaction levels.
• Overall findings: User satisfaction is moderately high (7.5/10), indicating a generally positive reception. However, the ease of use score (6.8/10) and qualitative feedback highlight significant usability issues for new users.
• Actionable next steps based on findings: Simplify the user interface to improve the experience for new users, potentially increasing overall satisfaction and ease of use scores.

Conducting mixed methods research with Maze

Mixed methods research is one of the most effective ways to boost your UX insights, and gather a more rounded understanding of your users’ problems and perspectives. Combining research methods and types of data can uncover insights you may otherwise miss. And while there are ideal times to conduct qualitative, quantitative, or mixed methods research, ultimately it really is as simple as more research = more insights .

If you’re looking for the ideal research companion to help conduct mixed methods research, consider Maze. Maze is the user research platform that empowers all teams with the research methods they need to get game-changing insights. Whether it’s a mixed methods study or a one-off test—Maze helps you gather accurate insights, faster, for more informed decision-making.

Frequently asked questions about mixed methods research

What is the purpose of mixed methods research?

The purpose of mixed methods research is to combine quantitative and qualitative data to provide a more complete understanding of a research problem. This approach helps validate findings, explore complex issues from multiple perspectives, and produce more reliable and actionable results.

What’s the difference between qualitative and quantitative research?

• Qualitative research explores non-numerical data to understand concepts, opinions, or experiences. It uses methods like interviews, focus groups, and observations to gather in-depth insights.
• Quantitative research focuses on numerical data to quantify variables and uncover patterns. It uses methods like surveys, experiments, and statistical analysis to measure and analyze data.

What is the difference between mixed methods and multiple methods?

Mixed methods research integrates qualitative (e.g. interviews) and quantitative (e.g. surveys) data within a single study. Multiple methods research uses various research approaches, but they can be either qualitative or quantitative. For example, it might use surveys and experiments (quantitative) or interviews and focus groups (qualitative) in different parts of a study without combining the data.

Quantitative Market Research: Fundamentals, Methods, and Applications

• by Alice Ananian
• August 16, 2024

Did you know that 99% of successful businesses use data to drive their decisions? In our increasingly digital world, quantitative market research has become an essential tool. It doesn’t just provide random facts; it offers precise insights into consumer behavior, market trends, and competitive landscapes, giving businesses the edge they need to storm ahead.

This article explores the fundamentals, methods, and applications of quantitative market research, helping business owners, marketing professionals, and entrepreneurs improve their decision-making and drive their businesses forward.

What is Quantitative Market Research?

Quantitative market research is a methodical approach to gather and analyze numerical data, offering businesses a practical understanding of customer behavior and market trends.

This can be part of both primary and secondary market research. Quantitative market research predominantly relies on structured tools like surveys, polls, and questionnaires to collect quantifiable pieces of information such as percentages, frequencies, and ratings. This research is carried out on a large, representative sample of the target audience to ensure accurate reflection of widespread attitudes and behaviors.

Following the data collection, statistical techniques are applied to reveal patterns, track trends, and identify relationships, effectively converting raw data into actionable insights to guide marketing strategies.

Quantitative vs. Qualitative Research

To fully appreciate quantitative research, it’s essential to understand how it differs from qualitative market research:

While quantitative research provides broad, generalizable insights, qualitative research offers deeper, context-rich understanding. Many successful market research strategies combine both approaches to gain a comprehensive view of the market.

Applications of Quantitative Market Research

Quantitative market research finds applications across various business functions and industries. Here are some key areas where this research method proves invaluable:

Product Development

• Measuring consumer preferences for product features: This involves surveying potential customers to rank or rate different product features, helping companies prioritize which features to include or improve.
• Assessing market demand for new products: Researchers can use quantitative methods to estimate the potential market size and gauge consumer interest in a new product concept before investing in development .
• Evaluating pricing strategies: Through techniques like conjoint analysis or price sensitivity meters, companies can determine optimal price points that maximize both sales and profitability.

Brand Management

• Tracking brand awareness and perception: Regular surveys can measure how many consumers recognize a brand and what associations they have with it, allowing companies to monitor their brand’s health over time.
• Measuring brand loyalty and customer satisfaction: Quantitative research can assess how likely customers are to repurchase or recommend a brand, providing insights into customer retention strategies.
• Comparing brand performance against competitors: Competitive benchmarking surveys can reveal a brand’s strengths and weaknesses relative to competitors in various attributes.

Customer Segmentation

• Identifying distinct customer groups: By analyzing survey data on demographics, behaviors, and preferences, researchers can use cluster analysis to group customers with similar characteristics.
• Determining the size and value of different market segments: Once segments are identified, quantitative research can estimate the size of each segment and its potential value to the business.

Advertising Effectiveness

• Measuring ad recall and recognition: Surveys conducted after ad campaigns can quantify how many people remember seeing an ad and can correctly identify the brand associated with it.
• Assessing the impact of advertising on purchase intent: Researchers can measure how exposure to ads influences consumers’ likelihood to buy a product, helping to justify advertising spend.
• Evaluating return on investment for marketing campaigns: By linking advertising exposure data with sales data, companies can calculate the ROI of their marketing efforts.

Market Sizing and Forecasting

• Estimating market size and growth potential: Using survey data and secondary sources, researchers can quantify the current market size and project future growth based on trends and economic factors.
• Projecting future sales and market share: Time series analysis and regression models can be used to forecast a company’s sales and market share based on historical data and market conditions.

Customer Experience

• Measuring customer satisfaction and loyalty: Regular surveys can track customer satisfaction scores and Net Promoter Scores (NPS) to gauge overall customer sentiment and loyalty.
• Identifying pain points in the customer journey: Quantitative analysis of customer feedback can highlight common issues or areas of dissatisfaction in the customer experience.
• Quantifying the impact of service improvements: By measuring customer satisfaction before and after implementing changes, companies can assess the effectiveness of their improvement initiatives.

Competitive Analysis

• Benchmarking product or service performance: Surveys can compare how a company’s offerings stack up against competitors on various attributes, helping identify areas for improvement.
• Assessing market share and competitive positioning: Regular tracking studies can monitor changes in market share and brand positioning relative to competitors, informing strategic decisions.

Benefits and Challenges of Quantitative Market Research

Quantitative market research offers a range of advantages that make it a valuable tool for businesses seeking data-driven insights. Understanding these benefits can help organizations leverage this research method effectively to inform their strategies and decision-making processes.

Objectivity: Quantitative research provides unbiased, numerical data that can be statistically analyzed. This objectivity ensures that the findings are not influenced by the researcher’s personal biases or perspectives.

Generalizability: Results derived from large sample sizes can be extrapolated to represent the broader population. This means that the findings are more likely to be valid for all individuals within the target group, enhancing the reliability of the study.

Comparability: Standardized data collection methods allow for easy comparison across different time periods or market segments. This comparability is crucial for tracking changes and trends over time, as well as for identifying differences between various subgroups.

Scalability: Quantitative research methods can efficiently gather data from large sample sizes. This scalability makes it possible to conduct studies on a much larger scale, providing more comprehensive insights into the research question.

Hypothesis testing: Quantitative research enables researchers to test specific theories or assumptions about market behavior. By confirming or disproving these hypotheses, researchers can gain a deeper understanding of the factors driving market trends and consumer behaviors.

Decision support: The concrete data obtained from quantitative research provides a solid foundation to support strategic decision-making. This evidence-based approach facilitates more informed and effective decisions, reducing the risk of error and improving outcomes.

While quantitative market research provides numerous advantages, it’s important to recognize that this approach also comes with its own set of limitations and potential pitfalls. Being aware of these challenges can help researchers and businesses plan more effectively and interpret results with appropriate caution.

Limited depth: Quantitative research methods may not capture the nuanced reasons behind consumer behavior or attitudes, often resulting in a superficial understanding of complex issues.

Inflexibility: Structured surveys and experiments may miss unexpected insights that could emerge in more open-ended research methods, limiting the scope of discovery.

Response bias: Respondents may not always provide honest or accurate answers, particularly on sensitive or personal topics, leading to skewed data and unreliable conclusions.

Cost: Conducting large-scale surveys or experiments can be expensive, often requiring significant financial resources for data collection, participant incentives, and analysis.

Time-consuming: The proper design, implementation, and analysis of quantitative research can be time-intensive, potentially delaying the results and impacting project timelines.

Expertise required: Quantitative research requires extensive knowledge of statistical analysis and research methodologies, necessitating skilled professionals to ensure accurate and reliable outcomes.

Examples of Quantitative Market Research

To illustrate the practical applications of quantitative market research, let’s explore some real-world examples:

Netflix A/B Testing Titles

Ever noticed how Netflix displays different titles or artwork for the same movie or show depending on your profile? This is A/B testing, a form of quantitative research. Netflix uses surveys and click-through rates to determine which title or artwork generates the most clicks and engagement.

Spotify Optimizing Playlists

How does Spotify create those eerily perfect playlists that seem to know exactly what you’re in the mood for? Quantitative research plays a role! Spotify analyzes user listening habits, including skip rates, play time, and song popularity, to curate playlists that resonate with different user preferences.

Coca-Cola Testing New Flavors

Developing a new beverage flavor requires understanding consumer preferences. Coca-Cola uses surveys and taste tests to gather quantitative data on sweetness levels, flavor combinations, and overall appeal. This data helps them refine new flavors before a full-scale launch.

Apple gauging iPhone Screen Size Preferences

Before increasing iPhone screen sizes, Apple likely conducted quantitative research. Online surveys and focus groups could have gathered data on user preferences for screen size, one-handed usability, and content viewing experience. This data likely helped Apple determine the optimal screen size for future iPhones.

Dominos Revamping its Pizza Recipe

In 2009, Domino ‘s faced declining sales. Quantitative research came to the rescue. Domino’s conducted customer surveys and taste tests to understand customer dissatisfaction with its pizza crust and sauce. Based on the findings, they revamped the recipe, leading to a significant turnaround in customer satisfaction and sales.

These are just a few examples, but they showcase the power of quantitative research in helping businesses make data-driven decisions that resonate with their target audiences.

Tools and Resources for Quantitative Research

To conduct effective quantitative market research, consider utilizing these tools and resources :

Survey Platforms

Qualtrics : Comprehensive survey software with advanced analytics

Prelaunch : Lets you gather data via a landing page that concisely presents your product 

SurveyMonkey : User-friendly platform for creating and distributing surveys

Google Forms : Free tool for basic surveys and data collection

Statistical Analysis Software

SPSS : Powerful software for complex statistical analysis

R : Open-source programming language for statistical computing

Prelaunch : The platform is a comprehensive concept-validating tool that complies and presents the data you gather via your product’s landing page into insightful section that make it easier to make data-driven decisions.

Excel : Suitable for basic data analysis and visualization

Online Panel Providers

Dynata : Large global panel for diverse respondent recruitment

Amazon Mechanical Turk : Platform for crowdsourcing survey participants

Data Visualization Tools

Tableau : Creates interactive data visualizations and dashboards

Power BI : Microsoft’s business analytics tool for data visualization

Datawrapper : User-friendly tool for creating charts and maps

Market Research Associations

ESOMAR : Global voice of the data, research, and insights community

Insights Association : Leading voice, resource, and network of the marketing research and data analytics community

Academic Resources

Journal of Marketing Research : Scholarly journal featuring cutting-edge research methodologies

Market Research Society (MRS) : Provides training, qualifications, and resources for market researchers

Remember to choose tools that align with your research objectives, budget, and level of expertise. Many of these platforms offer free trials or basic versions, allowing you to experiment before committing to a paid solution.

Quantitative market research is a powerful tool for making data-driven decisions. By providing objective, measurable insights into consumer behavior and market trends, it helps businesses develop targeted strategies and stay ahead of the competition.

While it has its limitations, combining quantitative methods with qualitative approaches can offer a comprehensive market understanding. Careful planning, rigorous methodology, and thoughtful interpretation of results are key to successful quantitative research.

Embrace the power of numbers and let data guide your business success.

Alice Ananian

Alice has over 8 years experience as a strong communicator and creative thinker. She enjoys helping companies refine their branding, deepen their values, and reach their intended audiences through language.

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Qualitative research coding methods for 2024.

Home » Qualitative Research Coding Methods for 2024

In the realm of qualitative research, Advanced Qualitative Coding plays a pivotal role in extracting meaningful insights from complex data sources. As researchers face ever-increasing volumes of interviews, texts, and multimedia content, the need for efficient and sophisticated coding methods has become paramount. By utilizing advanced techniques, researchers can not only categorize data more effectively but also enhance the depth of their analysis, ensuring richer outcomes.

As we move into 2024, embracing these advanced coding methods is essential for addressing contemporary research challenges. The latest techniques incorporate AI-driven tools that streamline the coding process while minimizing bias and maximizing reliability. Understanding the nuances of these methods is critical for researchers aiming to elevate their work and uncover valuable insights. This section will explore the foundational concepts and innovative practices in Advanced Qualitative Coding, setting the stage for deeper exploration throughout this document.

Trends in Qualitative Research Coding

The landscape of qualitative research coding is evolving rapidly, with several noteworthy trends emerging in 2024. One significant trend is the move towards advanced qualitative coding , which integrates technology and traditional methodologies. Researchers are increasingly utilizing advanced software tools to streamline the coding process and uncover deeper insights within qualitative data. This shift not only enhances efficiency but also promotes data visualization, helping to identify patterns and themes more effectively.

Another important trend is the emphasis on collaborative coding practices. As research teams become more diverse, the need for shared coding frameworks is critical. These frameworks encourage collective analysis, allowing researchers to compare interpretations and derive richer insights. Moreover, there is growing recognition of the importance of coding reliability, prompting researchers to adopt robust validation methods for their coding processes. By embracing these trends, qualitative researchers can enhance their analytical capabilities, resulting in more meaningful and actionable insights for diverse applications.

Incorporating Technology in Coding Methods

Incorporating technology in coding methods greatly enhances the efficiency and accuracy of Advanced Qualitative Coding. By utilizing specialized software, researchers can automate the initial stages of data analysis, allowing them to focus on deeper interpretations. Advanced tools can quickly identify patterns and themes within qualitative data, minimizing the risk of bias that comes with manual coding. This also streamlines collaboration among researchers, as insights can be easily shared and revisited as needed.

Furthermore, integrating technology facilitates a more structured approach to qualitative research. Data visualization features and analytical dashboards help researchers to present findings in a more digestible format. One significant advantage is the ability to track changes and iterations in coding, creating a transparent audit trail. Overall, the incorporation of technology not only enhances the rigor of qualitative analysis but also significantly reduces the time invested in data processing.

Evolution of Traditional Coding Techniques

Traditional coding techniques in qualitative research have undergone significant transformations over the years. Initially, coding involved a manual process where researchers would meticulously read through transcripts line by line, assigning codes based on emerging themes. This labor-intensive method often resulted in inconsistencies and subjective interpretations, hampering the reliability of findings. As the demand for more efficient and accurate analysis grew, researchers began exploring advanced qualitative coding, introducing software tools that could automate parts of this process.

With advanced qualitative coding, modern techniques now incorporate machine learning algorithms and data analytics, enhancing the coding process. These tools allow researchers to analyze larger datasets more efficiently, improving both the speed and accuracy of coding. As these technologies evolve, they not only streamline workflow but also promote collaboration among team members. Ultimately, this evolution signifies a pivotal shift towards embracing technology in qualitative research, enabling researchers to extract deeper insights from their data while minimizing the shortcomings of traditional methods.

Advanced Qualitative Coding Techniques

Advanced qualitative coding techniques involve sophisticated methods for analyzing qualitative data beyond the basics. To effectively harness these techniques, researchers can embrace a combination of thematic coding, grounded theory, and framework analysis. Each method provides unique insights, allowing researchers to explore data in-depth.

Thematic Coding : This technique helps identify patterns and themes that emerge from the data. It emphasizes understanding the underlying meanings and relationships within the text, facilitating a deeper exploration of respondents' perspectives.

Grounded Theory : Grounded theory involves developing theories based on the data collected. Researchers iteratively analyze data and generate concepts, allowing for a flexible yet systematic exploration of the subject matter.

Framework Analysis : This approach offers a structured framework for data analysis. It enables researchers to sort data into key themes, facilitating comparison and contrast across different cases or respondents.

These advanced qualitative coding methods help researchers gain a comprehensive understanding of complex qualitative data, leading to richer and more informed insights.

Leveraging AI for Advanced Qualitative Coding

In recent years, AI technologies have revolutionized the field of qualitative research. These advancements allow researchers to conduct advanced qualitative coding, making the process more efficient and accurate. By employing AI tools, researchers can automate tasks such as transcription and data analysis, thus freeing up valuable time for deeper insight exploration. This transformation enhances the quality of the analysis, mitigating the potential for human biases that can arise during manual coding.

Moreover, AI can categorize and identify themes in qualitative data with remarkable speed and precision. It enables researchers to uncover patterns that might be overlooked in manual examinations. Implementing AI for advanced qualitative coding not only streamlines workflows but also enriches the overall quality of research findings. As we move into 2024, the integration of AI into qualitative methods will be indispensable for researchers aiming to achieve sophisticated and trustworthy results in their studies.

Mixed-Methods Approaches: Combining Quantitative and Qualitative Data

Mixed-methods approaches integrate both quantitative and qualitative research to provide a fuller understanding of a phenomenon. These methods enhance data richness by allowing researchers to triangulate findings, ensuring that results are both statistically significant and deeply contextual. By combining numerical data with narrative accounts, researchers can reveal patterns and insights that might remain hidden when using a single method.

A successful mixed-methods study involves several key steps. First, researchers should define clear objectives that justify the use of both approaches. Next, it's essential to collect data in tandem, ensuring that qualitative insights logically complement quantitative results. Finally, the analysis phase must carefully weave together disparate data types, allowing for a comprehensive interpretation of findings. This nuanced approach ultimately supports advanced qualitative coding, drawing on diverse perspectives to enhance overall research quality.

Conclusion: The Future of Advanced Qualitative Coding in Research

The future of advanced qualitative coding in research holds promising advancements that can significantly enhance qualitative methodologies. Researchers are increasingly turning to technologies that simplify the coding process while ensuring rich data insights. By integrating user-friendly tools, researchers can extract meaningful patterns without needing extensive training in qualitative analysis.

As we progress, the accessibility of advanced qualitative coding will empower diverse researchers to conduct thorough analyses. This democratization of technology means that insights will be more representative of varied populations and contexts. Ultimately, embracing innovation in qualitative coding will elevate the quality of research and lead to more reliable findings across disciplines.

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• Published: 16 August 2024

Assessment of work safety analysis performance among rural hospitals of Chirumanzu district of midlands province, Zimbabwe

• Tapiwa Shabani 1 ,
• Steven Jerie 1 &
• Takunda Shabani 1  

BMC Health Services Research volume  24 , Article number:  938 ( 2024 ) Cite this article

Metrics details

Ensuring workplace safety for healthcare workers is vital considering the important role they play in various societies which is to save life. Healthcare workers face different risks when performing tasks in various departments within hospitals, hence there is a need to assess work safety analysis procedures among healthcare workers. As a result, this study aims to assess the effectiveness of work safety analysis procedures among healthcare workers at Muvonde and Driefontein Sanatorium rural hospitals in Chirumanzu district. The research applied the descriptive cross-sectional design, combining quantitative and qualitative data collection methods. A questionnaire with both closed and open ended questionnaire was used for data collection among 109 healthcare workers at Muvonde hospital and 68 healthcare workers at Driefontein Sanatorium hospital. Secondary data sources, observations and interviews were also included as data collection methods. Quantitative data collected during the study was analysed using SPSS version 25. Braun and Clarke (2006)’s six phase framework was applied for qualitative data analysis. Ethical approval form was obtained from the District Medical Officer and Midlands State University. Findings of the study indicated that risks identified at Muvonde and Driefontein Sanatorium rural hospitals are classified as ergonomic, physical, chemical, psychosocial and biological risks. Respondents specified that these risks occur as a result of inadequate equipment, poor training, negative safety behaviour, poor management and pressure due to high workload. Safety inspection, safety workshops and monitoring of worker’s safety behaviour were mentioned as measures to manage risks. However, the strengths and weaknesses of the current safety procedures need to be assessed to highlight areas for improvement to reduce occurrence of risks within the hospitals.

Peer Review reports

Introduction

Professionals in hospitals save the lives of people in various communities as a result, this job requires a high level of commitment [ 1 , 2 ]. However, the community must not forget that healthcare workers who save their lives are exposed to various risks during work. [ 3 ] opined that the nature of tasks performed by healthcare workers in hospitals expose them to risks. Healthcare facilities, including rural hospitals, are work environments where workers are exposed to different work-related risks regularly [ 4 , 5 ]. Duties performed by healthcare workers involve lifting and transferring patients, dealing with patients with unpredictable behaviour, and handling infectious materials and exposure to chemicals [ 6 , 7 , 8 ]. This implies that healthcare workers are affected with both ergonomic, biological, chemical, psychosocial and physical risks. However, the protection of healthcare workers from risks depends on the effectiveness of work safety analysis procedures within the healthcare facility [ 9 , 10 ]. Therefore, guaranteeing the efficiency of work safety analysis procedures is important for reducing risks affecting healthcare works and creating a safe work environment. In the healthcare industry, where employees are exposed to various risks, it is essential to have effective work safety analysis procedures in place to minimize the risks of accidents and injuries [ 2 , 11 ].

Insufficient work safety measures can prompt a scope of negative results in the healthcare facilities [ 12 , 13 ]. According to [ 14 ] and [ 15 ] results of inadequate work safety analysis procedures within the workplace include work-related illness, injuries, accidents, stress and reduced productivity. Ergonomic risks affecting healthcare workers as a result of tasks they perform expose them to upper and lower back pain, muscular strain and neck pain [ 16 , 17 ]. In healthcare facilities healthcare workers always report high levels of musculoskeletal injuries related to ergonomic risks. Musculoskeletal disorders expose healthcare workers to acute and chronic injuries [ 8 , 18 , 19 ]. According to [ 20 ] and [ 21 ] healthcare workers are also affected with work-related pressure which expose them to stress, fatigue and anxiety which are categorised as psychosocial risks. Work-related stress is common among healthcare employees as a result of long working hours, shift work and dealing with patients who are critically ill [ 22 , 23 ]. Biological risks affecting healthcare workers globally expose them to tuberculosis, HIV/AIDS and hepatitis B/C [ 24 , 25 ]. In healthcare facilities, piercing materials such as needles are used; however, they result in sharp injuries among healthcare workers [ 26 , 27 ]. Nurses are mostly affected with physical risks such needle stick injuries and pricks/cuts as well as ionizing and non-ionizing radiation [ 28 , 29 ]. Allergies, eye and skin irritation are affecting hospital workers as a result of different chemicals used during hospital procedures [ 30 , 31 ]. This denotes that healthcare workers are exposed to chemical risks when performing their duties.

In sub-Saharan Africa the issue of work-related risks affecting healthcare workers increase as a result of ineffective safety procedures [ 32 , 33 ]. This means poor work safety analysis, negative safety behaviour, inadequate resources and poor safety training expose healthcare workers to risks. In less developed countries hospital employees are affected with occupational risks due to shortage of labour [ 4 , 5 ]. Due to shortage of labour in African countries during the outbreak of Covid-19 they increased the time of shifts for healthcare workers to cope with the high rate of hospitalization [ 34 , 35 ]. However, this increases mental and physical exhaustion among healthcare workers. In developed countries measures used to manage risks affecting workers in healthcare facilities are effective compared to developing countries [ 6 , 10 , 16 ]. This occurs because in developing countries such as Zimbabwe the use of effective work safety analysis procedures is commonly applied in timber, mining and manufacturing companies neglecting healthcare institutions.

Health workers are usually vulnerable to work related risks since issues of safety are always neglected in the health sector, specifically in hospitals located in marginal areas [ 36 , 37 ]. Therefore, this study assesses the effectiveness of work safety analysis procedures among healthcare workers at Muvonde and Driefontein Sanatorium rural hospitals in Chirumanzu district. As rural healthcare facilities Muvonde and Driefontein Sanatorium hospitals, they face unique challenges related to staffing constraints, resource limitations and infrastructure deficiencies. As a result, assessing the effectiveness of work safety analysis procedures at Driefontein Sanatorium and Muvonde rural hospitals is significant for understanding the existing safety protocols and identifying areas for improvement that are tailored to their operational context.

Through a thorough evaluation of the work safety analysis performance in these particular healthcare facilities, the research pinpoint important areas that require improvement and create focused recommendations to improve workplace safety procedures. This will result in the use of cutting-edge technologies for risk assessment and hazard identification, the creation of a culture of continuous improvement in work safety procedures, the introduction of new safety protocols, and the execution of customized training programs. Ultimately, the research findings may improve the health and safety of medical staff in remote hospitals while also acting as a template for raising occupational health and safety standards in similar environments around the world. The findings of the study would help the rural healthcare centres to achieve the demands of Sustainable Development Goal number 3 which focus on good health and well-being.

Materials and methods

Descriptive cross-sectional design was used during the study. The study was conducted at Muvonde and Driefontein Sanatorium rural hospitals. The two hospitals serve as referrals for clinics and other hospitals in Chirumanzu district and outside Chirumanzu district. The study population were medical and paramedical staff within the hospitals. The sample size was calculated using Yamane (1967) formula shown below:

Where: n is the sample size, N is the total population and e is the margin of error.

After calculations a sample of 68 healthcare workers were selected as questionnaire respondents at Driefontein Sanatorium hospital and 109 healthcare workers were selected as questionnaire respondents at Muvonde hospital as indicated by Table  1 . Healthcare workers who participated as questionnaire respondents were selected randomly from every strata. Key informants interviewees were selected purposively. A questionnaire with both closed and open ended questionnaires was prepared and self-administered during data collection to reduce margin of error. The questionnaire is shown in Appendix 1 . The pilot study of the questionnaire consisted of 10% of the participants from each of the two rural hospitals which were considered during the study. This conformed to [ 38 ] that 10% of the target population is used for pilot study before the main survey is done. At Muvonde hospital 10% of 109 questionnaire respondents were considered during the pilot study. This entails that 11 healthcare workers participated during the pilot study of the questionnaire at Muvonde hospital. At Driefontein Sanatorium hospital 10% of 68 healthcare workers were selected for pilot study. This clearly means 7 healthcare workers were taken as participants during the pilot study at Driefontein Sanatorium hospital. The participants who take part during the pilot study provide their suggestions and recommendations on how to improve the drafting of questionnaire items. Test-retest reliability has also been used to assess response stability over time, making sure that the questionnaire produced consistent answers when it was administered again. Experts in healthcare management and work safety analysis examine the questionnaire items to make sure they comply with industry best practices and standards in order to improve validity. To further increase validity and reliability, a pilot test including a sample of rural hospitals in Chirumanzu District was conducted to evaluate the questions’ clarity and relevance.

Semi-structured interviews were prepared to conduct interviews with the Nurse in Charge, Hospital Manager (Matron), Medical Superintendent, Head of Environmental Health department and Human Resource Manager at each rural hospital participating during the interviews. The District Medical Officer and National Social Security Officer were also taken as interviewees to collect the information regarding the objectives of the study. Observations were carried out using an observation checklist focusing much on work environment, equipment, duties performed by healthcare workers and safety procedures used within the hospitals. Rural hospitals’ weekly inspection reports, monthly reports, annual reports and incidents reports as well as review articles and journals were used as secondary data sources.

Quantitative data collected using a questionnaire was analysed using Statistical Package for Social Sciences version 25.0. Quantitative data was presented in the form of tables, pie-charts and graphs which were produced by the SPSS. Braun and Clarke (2006)’s six phase framework for doing thematic analysis was applied during qualitative data analysis. Ethical approval forms were obtained from the District Medical Officer and Midlands State University before data collection starts. All participants participated voluntarily. Every participant was enlightened that participating during the study was voluntary.

Socio-demographic characteristics

Demographic characteristic according to gender, age and marital status.

Table  2 presents the demographic characteristics of the respondents according to gender, age and marital status. From the results, the majority (70%) of the healthcare workers who participated during the study at Muvonde hospital were females. At Driefontein Sanatorium hospital most (69%) of the questionnaire respondents were females. At Muvonde hospital (26.6%) of the participants identified 34–41 years as their age group while at Driefontein Sanatorium hospital revealed that they were aged 34–41 years as indicated in Table  2 . The majority (47.7%) respondents at Muvonde hospital are married and at Driefontein Sanatorium hospital majority (61.8%) of the healthcare workers who participated during the study were married.

Demographic characteristics according to level of education and work experience

Table  3 shows demographic characteristics of the healthcare workers according to level of education and work experience. Based on the feedback provided by the questionnaire, the respondent majority (52.3%) of the participants at Muvonde hospital are holders of diplomas. At Driefontein Sanatorium hospital most (54.4%) designated diplomas as their highest level of education as indicated in Table  3 . Results in Table  3 demonstrate that the majority (42.2%) of the questionnaire respondents at Muvonde hospital specified their work experience between 5 and 10 years. Most (41.2%) of the healthcare workers who participated as questionnaire respondents at Driefontein Sanatorium hospital indicated their work experience between 5 and 10 years.

Risks identified at muvonde and driefontein sanatorium rural hospitals

Different types of risks were identified at Muvonde and Driefontein Sanatorium rural hospitals notably ergonomic, chemical, biological, physical and psychosocial risks as illustrated by Fig.  1 . Majority (44%) of the questionnaire respondents at Muvonde hospital identified ergonomic risks as the main risks affecting them. However, at Driefontein Sanatorium hospital most (30.9%) of the healthcare workers who participated as questionnaire respondents reported biological risks as the risk which mainly affect them at work. At Driefontein Sanatorium hospital psychosocial risks recorded the least percentage (10.3%) while at Muvonde hospital psychosocial risks recorded (11.9%) as designated by the results shown in Fig.  1 .

Types of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Distribution of risks identified at muvonde and driefontein sanatorium hospital

Distribution of ergonomic risks.

The distribution of ergonomic risks is illustrated by Fig.  2 . Based on the findings of the study on distribution of ergonomic risks, the majority (33.9) reported standing for a long time while (20.2%) specified repetitive work at Muvonde hospital as shown in Fig.  2 . Some (17.4%) of the participants indicated lifting patients, manual therapy (11.1%), uncomfortable position (10.1%) and lifting of medical devices was reported by (6.2%) of questionnaire respondents at Muvonde hospital. Based on the results of distribution of ergonomic risks obtained at Driefontein Sanatorium hospital majority (27.9%) reported standing for a long time, lifting of patients (25%), repetitive work (17.6%), manual therapy (11.8%) and (5.9%) indicated lifting of medical devices as a concern as illustrated by Fig.  2 .

Distribution of physical risks

Regarding the distribution of physical risks at Muvonde hospital the majority (28.4%) reported sharp injuries, extreme temperatures (cold/hot) (24.8%), noise (22%), slips and falls (15.6%), radiation (5.5%) and electric shock (3.7%). Results obtained at Driefontein Sanatorium hospital indicated that the majority (30.9%) of the study respondents specified sharp objects, extreme temperatures (cold/hot) (26.5%), noise (20.6%), slips and falls (13.2%), radiation (5.9%) and electric shock (2.9%). The distribution of physical risks is indicated in Fig.  2 .

Distribution of biological risks

Majority (39.4%) of the healthcare workers who participated as questionnaire respondents at Muvonde hospital reported blood spillage while at Driefontein Sanatorium hospital the majority (31%) regarded breathing contaminated as the major biological risk Fig.  2 . Findings at Muvonde hospital shows that (17.4%) of the study participants specified breathing contaminated air, vomitus, sputum or urine of patients (16.5%), contact with wounds (15.6%) and viral infection (11%) was considered as the least biological risk among the biological risks. Furthermore, at Driefontein Sanatorium hospital (25%) of questionnaire respondents stated blood spillage, viral infections (16%), vomitus, sputum or urine of patients (15%) and contact with wounds (13%) were reported as biological risks.

Distribution of chemical risks

The study (Fig.  2 ) provides data about the distribution of chemical risks at Muvonde hospital as sanitizers (29%), cleaning detergents (24%), latex gloves (20%), anaesthetic gases and sterilizing agents (14%) and mercury (13%). Sanitizers (28%), cleaning detergents (21%), latex gloves (19%), anaesthetic gases and sterilizing agents (19%) and mercury (13%) were reported by questionnaire respondents as chemical risks prevailing at Driefontein Sanatorium hospital as shown in Fig.  2 .

Distribution of psychosocial risks

Regarding the distribution of psychosocial risks at Muvonde hospital majority (33%) reported dealing with very ill patients, overwork (26.6%), verbal abuse (16.5%), fatigue (11.9%), physical abuse (6.4%) and problems with the top management was specified by (5.5%) of participants Fig.  2 . During the study at Driefontein Sanatorium hospital the majority (39.7%) of the questionnaire respondents indicated dealing with severely ill patients however, (26.5%) specified overwork, and (13.2%) indicated verbal abuse, (11.8%) reveals fatigue, and (4.4%) reported physical abuse and (4.4%) stated problems with the top management as a risk among psychosocial risks.

Distribution of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Causes of risks identified at muvonde and driefontein sanatorium hospital

Study participants were asked to indicate causes of risks identified at Driefontein Sanatorium hospital and Muvonde hospital. Based on the findings shown in Fig.  3 majority (20.2%) of the healthcare workers who participated as questionnaire respondents at Muvonde hospital reported that risks occur as a result of pressure due to high workload, followed by (17.4%) who specified shortage of labour, (14.7%) indicated inadequate equipment, (3.7%) designated age, gender (10.1%), poor trainings (6.4%) and negative safety behaviour (6.4%). However, findings of the study at Muvonde hospital indicated that (8.3%) of the respondents reported poor management, department the worker is allocated (5.5%) and (7.3%) of the healthcare workers reported that use of personal protective equipment/cloth for a long time exposes them to risks Fig.  3 .

At Driefontein Sanatorium hospital the majority (25%) specified that they are exposed to risks as a result of pressure due to high workload, followed by (16.2%) who stated shortage of labour, (11.8%) indicated inadequate equipment and (2.9%) and (4.4%) pointed out age and gender as the factors which expose them to risks respectively Fig.  3 . Results obtained at Driefontein Sanatorium hospital shows that (8.8%) of the study participants indicated that they are exposed to risks as a result of poor training and this was similar to (8.8%) healthcare workers who indicated negative safety behaviour as a factor which expose healthcare workers to risks. Poor management was designated by (7.4%), the department the worker is allocated was specified by (5.9%) and (8.8%) use of personal protective equipment/cloth for a long time were reported as a factors which expose healthcare workers to occupational risks in hospitals as indicated by Fig.  3 .

Causes of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Effects of risks identified at muvonde and driefontein sanatorium hospital

Effects of ergonomic risks.

Study findings at Muvonde hospital shows that the majority (51.4%) specified back injuries regarding effects of ergonomic risks Fig.  4 . Based on the results (21.1%) reported neck pain while (16.5%) indicated shoulder discomfort followed by muscular strain which was designated by (11%) of the questionnaire respondents at Muvonde hospital. Regarding effects of ergonomic risks at Driefontein Sanatorium hospital most (57.4%) indicated back injuries while neck pain and shoulder discomfort was reported by (19.1%) and (13.2%) respectively Fig.  4 . However, at Driefontein Sanatorium hospital muscular strain was designated as an effect of ergonomic risks by (10.3%) of the study participants.

Effects of biological risks

Regarding effects of biological risks at Muvonde hospital (29.4%) of the respondents reported Covid-19 virus while (6.4%) specified tuberculosis, (3.7%) indicated hepatitis B/C and very few (1.8%) designated HIV/AIDS Fig.  4 . However, the majority (58.7%) of the study participants at Muvonde hospital specified that none of the infections related to ergonomic risks. Based on the findings obtained at Driefontein Sanatorium hospital pertaining effects of ergonomic risks, the majority (33.8%) specified Covid-19 virus whereas (25%) reported tuberculosis, (13.2%) specified hepatitis B/C and (1.5%) indicated HIV/AIDS. Nonetheless, (26.5%) designated that they never experienced any infections related to biological risks at work as shown by Fig.  4 .

Effects of physical risks

Effects of physical risks were examined at Muvonde hospital and Driefontein Sanatorium hospital. Results shows that more than half (57.8%) of the questionnaire respondents at Muvonde hospital reported needlestick injuries. Figure  4 also indicated that (19.3%) of the healthcare workers at Muvonde hospital specified cuts/pricks, (14.7%) indicated influenza and (8.3%) of the study participants designated crumps. Based on the findings regarding effects of physical risks at Driefontein Sanatorium hospital most (52.9%) stated needle stick injuries. Cuts/pricks were reported by (27.9%) healthcare workers, (11.8%) specified influenza while (7.4%) designated crumps among effects of physical risks at Driefontein Sanatorium rural hospital.

Effects of chemical risks

Considering effects of chemical risks at Muvonde hospital most (39.4%) of the study participants reported skin irritation, followed by allergies specified by (33.9%) respondents, (11.9%) stated pulmonary irritation while (2.8%) of the participants identified asthma. However, (10.1%) of the questionnaire respondents indicated they never experienced effects of chemical risks related to tasks they perform at Muvonde hospital and the least (1.8%) indicated birth defects as effects of chemical risks Fig.  4 . At Driefontein Sanatorium hospital the majority (48.5%) of the healthcare workers who participated as questionnaire respondents specified skin irritation regarding effects of chemical risks. Allergies were reported by (26.5%) study participants, (17.6%) indicated pulmonary irritation and (4.4%) stated asthma among the effects of chemical risks they experienced at Driefontein Sanatorium hospital Fig.  4 . Nonetheless, very few (2.9%) did not report any effect of chemical risks at Driefontein Sanatorium hospital.

Effects of psychosocial risks

Majority (47.7%) of the questionnaire respondents at Muvonde hospital indicated stress as an effect of psychosocial risks Fig.  4 . However, some (20.2%) of the respondents reported fatigue, (15.6%) specified anxiety, (3.7%) stated insomnia, and (2.8%) of the participants pointed out persistent tiredness as effects of psychosocial risks they experienced at Muvonde hospital. Blood pressure was specified by the least (0.9%) of the healthcare workers at Muvonde hospital while (9.2%) of the healthcare employees specified that they never experienced any challenges related to psychosocial risks. Figure  4 also indicates effects of psychosocial risks reported by healthcare workers at Driefontein Sanatorium hospital. Most (45.6%) of the study participants at Driefontein Sanatorium hospital indicated that they experienced stress as a result of psychosocial risks. At Driefontein Sanatorium hospital fatigue was reported by (23.5%) respondents, anxiety was specified by (16.2%) participants, insomnia (5.9%) and (2.9%) participants indicated persistent tiredness. Nevertheless, minority (1.5%) of the questionnaire respondents indicated that they are affected with blood pressure as a result of psychosocial risks and some (4.4%) specified that they never experienced effects of psychosocial risks.

Effects of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Work safety measures used to manage risks identified at muvonde and driefontein sanatorium hospital

Study participants at Driefontein Sanatorium hospital and Muvonde hospital were requested to indicate work safety measures used for coping with work-related risks. Regarding work safety measures at Muvonde hospital the majority (38%) of the questionnaire respondents indicated personal protective equipment/cloth Fig.  5 . However, at Muvonde hospital (13%) of the study participants specified safety inspection, (12%) of healthcare workers reported proper waste disposal, (11%) designated monitoring of workers’ safety behaviour, (9%) safety training and (9%) of the respondents stated safety workshops as methods used to manage risks. Other (8%) of the healthcare workers who participated as questionnaire respondents indicated other measures that can be used to manage risks for example screening health workers for diseases such as hepatitis B/C virus, Covid-19 and tuberculosis as indicated by Fig.  5 .

Based on the results obtained at Driefontein Sanatorium hospital pertaining safety measures the majority (40%) stated personal protective equipment/cloth as indicated by Fig.  5 . At Driefontein Sanatorium hospital safety inspection was reported by (10%) of the questionnaire respondents, proper disposal of waste (9%), monitoring of workers’ safety behaviour (7%), safety training (6%) and safety workshops was specified by (6%) of the healthcare workers. However, Fig.  5 indicated that (22%) of the questionnaire respondents at Driefontein Sanatorium hospital stated other safety measures such as screening healthcare workers for diseases for example Covid-19, tuberculosis and hepatitis B/C.

During the study survey at Driefontein Sanatorium hospital the Matron indicated that, As a hospital which is focusing on maintaining high standard of sterility using available resources to promote quality healthcare service in Chirumanzu district and beyond we put safety informative charts for the benefit of both patients, visitors and workers. The Matron go on to indicate that we also provide safety facilities such as washing hand facilities. This was supported by observations results. During observations informative charts and washing hand facilities were observed at Driefontein Sanatorium hospital as shown by Plate 1 and Plate 2 respectively.

Informative chart shown at driefontein sanatorium hospital. Source Field Survey (2023)

Washing hand facility (bucket) observed at driefontein sanatorium hospital. Source Field Survey (2023)

Work safety measures used to manage risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Safety policies at muvonde and driefontein sanatorium hospital

The majority (47.7%) of the questionnaire respondents agree that at Muvonde hospital there are clear safety policies while (33.9%) strongly agree, (10.1%) disagree and (8.3%) strongly disagree Fig.  6 . However, at Driefontein Sanatorium hospital most (54.4%) of the healthcare workers agree and (27.9%) strongly agree about the availability of clear safety policies at Driefontein Sanatorium hospital. During the study at Driefontein Sanatorium hospital the researcher was given access to some of the hospital’s documents to use them as secondary data sources. The researcher discovered a safety policy manual and went through it and it was showing clear objectives. The objectives of the policy manual include 1 ) To provide continued guidance to health workers and students on infection prevention control measures and policies. 2 ) To promote an educational strategy for healthcare workers with a broader aim in mind. 3 ) To promote participation in infection prevention and control by healthcare workers, patients, relatives and visitors on how to reduce hospital acquired infection. 4 ) To allay unnecessary anxiety by providing fundamental information on infection prevention and control measures. 5 ) To promote, maintain and strengthen the high standard of cleanliness in the hospital and its environment. Appendix 2 only presents the cover page, preface, table of contents and objectives of hospital rules, regulations and policies related to infection prevention and control at Driefontein Sanatorium hospital. At Driefontein Sanatorium hospital few (10.3%) disagree and very few (7.4%) strongly disagree about the availability of clear safety policies at Driefontein Sanatorium hospital Fig.  6 .

Availability of clear safety policies at muvonde and driefontein sanatorium hospitals. Source Field Survey (2023)

Effectiveness of work safety measures used to manage risks at muvonde hospital and driefontein sanatorium hospital

Figure  7 shows that while a small percentage of survey respondents (7.3%) said that institutional measures used to manage risks at Muvonde hospital are poor, more than half (56%) said the measures are good, (21.1%) indicated that the measures are very good and (15.6%) specified that the measures are excellent. As seen in Fig.  7 , the majority of the study participants (51.5%) stated that the institutional measures in place at Driefontein Sanatorium hospital to manage risks are effective because they are good. This is followed by (30.9%) who said the measures are very good and (13.2%) stated that the measures are excellent. A small percentage (4.4%) of the questionnaire respondents at Driefontein Sanatorium hospital indicated that the safety measures are poor.

Effectiveness of work safety measures used to manage risks at muvonde and driefontein sanatorium hospitals. Source Field Survey (2023)

Association between Work Experience (years) and effectiveness of Work Safety measures used to manage risks

During data analysis Chi-Square test was employed to test the association between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

The following hypotheses were tested:

Null hypothesis (H 0 ) – There is no association between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

Alternative hypothesis (H 1 ) - There is an association between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

0.05 was set as the probability value.

If \(\:\:x\) 2 is above 0.05 accept H 0 and reject H 1 . There is no relationship between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

If \(\:\:x\) 2 is below 0.05 accept H 1 and reject H 0 . There is a relationship between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

Table  4 shows that the Chi-Square test results were 0.000, which is less than the significance level 0.05. In light of the findings, we accept H 1 and reject H 0 . Based on the analysis, the findings show that evaluating the effectiveness of work safety measures at Muvonde and Driefontein Sanatorium hospital is associated with work experience.

Females made up the majority of the healthcare workers who took part during the study conducted at Driefontein Sanatorium hospital and Muvonde hospital. The findings of the research conducted at Muvonde and Driefontein Sanatorium hospitals align with the findings concur with [ 39 ] that females constitute the majority of healthcare workers in the United States. A study carried by [ 40 ] also indicated that females constitute 70% of the healthcare workers working in healthcare facilities of Pakistan. This implies that healthcare workers who are men are less than women. The explanations for the gender gap in hospital employment are varied. For example, women are typically drawn to caregiving-related fields due to their nurturing disposition and empathy for others. The purpose of asking the gender of the respondents was to discover risks that are associated with gender.

Overall, data gathered for the study suggests that healthcare staff at Driefontein Sanatorium hospital and Muvonde hospital have a different age range, which offers a variety of perspectives and experiences about dangers impacting healthcare workers. Because this is the traditional age range for people to start their careers in medical institutions, the majority of healthcare workers are between the ages 26–33 and 34–41. Regarding the findings at Muvonde and Driefontein Sanatorium hospitals most of the healthcare personnel who took part during the study are in the active group. This concurs with a study conducted by [ 41 ] from healthcare facilities in Oriental Mindoro, which shows that the majority of hospital employees who took part in the study were between the ages of 26 and 33 which is the active group.

Most of the healthcare workers who participated as questionnaire respondents at Driefontein Sanatorium hospital and Muvonde hospital are married. Similar findings about married status were found at Muvonde hospital and Driefontein Sanatorium hospital. This could be due to the fact that hospital’ staff members prioritise marriage over pursuing their careers or because marriage is culturally valued in the communities where the hospitals are located. However, there is a connection between married status and the risk levels that impact healthcare workers. For instance, married healthcare workers have additional duties in addition to their jobs, which increases their exposure to risk. Healthcare workers’ responsibilities towards their families have a significant impact on how well they function and perform at work.

The majority of healthcare personnel who participated in the study at Muvonde and Driefontein Sanatorium hospitals indicated that their highest level of education was a diploma, however, some of them held certificates. This is mainly because the majority of medical training institutions in Zimbabwe provide hospital workers with diplomas and certificates rather than degrees. The inquiry concerning the level of education for healthcare workers was raised on the grounds that it influences how they might interpret potential risks at their work environment and how to alleviate them. Higher educated medical practitioners are more aware of the risks related to their work and they can prepare more effectively for occupational risks before they occur [ 42 ]. This suggests that there is an association between the level of education of healthcare practitioners and safety attitudes and safety practices. The results obtained at Muvonde hospital and Driefontein Sanatorium hospital demonstrated that most of the healthcare workers indicated 5–10 years range as their work experience. However, very few indicated 16 years or above as their work experience range. This proposes that turnover rates at Driefontein Sanatorium hospital and Muvonde hospital are high and this leads to the availability of less experienced and youthful workforce. Asking healthcare workers their work experience was vital because experienced hospital workers can be better equipped with better methods for managing risks when performing their tasks. According to [ 12 ] an association exists between work experience and effectiveness of safety measures used to manage work-related risks.

According to the results obtained at Driefontein Sanatorium hospital and Muvonde hospital ergonomic, chemical, psychosocial, biological and physical risks were identified. Regarding risks identified by healthcare workers at Muvonde hospital, the majority indicated ergonomic risks. Duties performed by healthcare workers involve manual tasks, pushing, transferring and lifting patients as well as repetitive tasks which expose them to ergonomic risks. Most (30.9%) of the healthcare workers at Driefontein Sanatorium hospital reported biological risks because the hospital deals mainly with contagious diseases such as tuberculosis. Some of the healthcare workers at both Muvonde and Driefontein Sanatorium hospital reported chemical risks. In hospitals healthcare employees are exposed to various types of chemicals such as disinfection chemicals used for cleaning and disinfecting equipment and facilities. According to [ 16 ] workers who perform their tasks in healthcare facilities are affected with various types of work-related risks such as psychosocial, physical, biological, ergonomic and chemical risks.

Based on the distribution of ergonomic risks at Driefontein Sanatorium hospital and Muvonde hospital, standing for a long time was reported by most of the questionnaire respondents. Duties performed by healthcare professionals require them to stand for long periods of time. Healthcare workers stand for a long time when providing services to patients. In hospitals workers usually stand in queues and move around the hospital on their feet helping patients on wheelchairs or stretchers to get their services. Results showing the distribution of physical risks at Muvonde hospital and Driefontein Sanatorium hospital indicated that sharp objects were specified by most of the healthcare workers who participated during the study. In healthcare facilities there is high use of sharp objects such as needles, razorblades, scalps and scissors however, if safety precautions are not followed when using them they expose workers to injuries, cuts and pricks. The findings of the study coincide with the findings of [ 4 ] which indicates that healthcare workers are exposed to sharp injuries as a result of continuous use of sharp objects in hospitals. Regarding distribution of biological risks at Driefontein Sanatorium hospital and Muvonde hospital, healthcare workers stated contact with wounds, viral infections, vomitus, urine of patients, breathing contaminated air and blood spillage. Blood spillage was reported as the major biological risk by the majority of healthcare employees. Healthcare workers are mainly exposed to blood spillages when carrying out surgeries and procedures. However, blood carries various types of contagious agents for example, hepatitis B, hepatitis C, HIV/AIDS and some blood-borne pathogens which can be transferred from one person to the other through contact with blood which is contaminated.

Regarding the distribution of chemical risks mercury, latex gloves, anaesthetic gases, sanitizers and cleaning detergents were reported by questionnaire respondents at Muvonde hospital and Driefontein Sanatorium rural hospitals. Majority of the healthcare workers who participated during the study stated sanitizers. This occurs because sanitizers are mostly used by healthcare workers after procedures to sanitize their hands in order to prevent cross infection. Sanitizer contains alcohol that kills bacteria; however, it exposes healthcare employees to skin irritation [ 17 , 31 ]. Findings of the study on distribution of psychosocial risks at Driefontein Sanatorium hospital and Muvonde hospital indicated fatigue, over work, abuse and dealing with severely ill patients. Nevertheless, most of the healthcare employees specified dealing with very ill patients. Caring severely ill patients is highly demanding which increases the rate of stress affecting workers in hospitals.

Results indicated that risks identified at Muvonde hospital and Driefontein Sanatorium hospital are caused by various aspects notably, use of personal protective equipment/cloth for a long time, department the worker is allocated at the hospital, poor management, negative safety behaviour, poor training, gender, age, inadequate equipment, shortage of labour and pressure due to high workload. Regarding the mentioned causes of risks at Muvonde and Driefontein Sanatorium hospitals, the majority of the healthcare workers specified pressure due to high workload. The increasing number of patients can prompt long working hours and increase workload to healthcare workers. As a result of this, healthcare professionals may experience physical and psychosocial side effects, including exhaustion, burnout and illnesses linked to stress caused by overwork. This implies that safety in hospitals is compromised by strain brought by heavy workload, putting healthcare personnel at risk.

Based on the results obtained at Muvonde and Driefontein Sanatorium hospital pertaining to ergonomic risks, respondents indicated muscular strain, shoulder discomfort, neck pain and back injuries. However, most of the healthcare workers reported back injuries. These findings imply that back injuries are a serious problem in both hospitals as a result medical practitioners need to pay attention to them. Because of the nature of their jobs, healthcare personnel frequently suffer from back injuries. Regular lifting, moving and transferring of patients puts a lot of strain on the backs of healthcare professionals. Furthermore, working in awkward positions is a common requirement for healthcare professionals, which can potentially lead to back injuries. Back injuries can also result from pushing large items like wheelchairs and hospital beds. Results of the study also indicate that Covid-19, HIV/AIDS and tuberculosis were indicated as effects of biological risks. At Muvonde hospital and Driefontein Sanatorium hospital influenza, cuts/pricks and needle stick injuries were specified as effects of physical risks. However, with the two rural hospitals the majority of the study participants stated needle stick injuries. The explanations behind such a high frequency of needle stick injuries at Driefontein Sanatorium hospital and Muvonde hospital could be due to poor training of healthcare workers on how to handle needle sticks with care. Additionally, in hospitals there is high use of needle sticks due to their piercing nature.

Healthcare professionals at Muvonde and Driefontein Sanatorium hospital have reported birth defects, skin irritation, allergies, asthma and pulmonary irritation as effects of chemical risks. Similar to Driefontein Sanatorium hospital, where the majority of healthcare workers (48.5%) reported skin irritation, the majority of hospital employees at Muvonde hospital (39.4%) expressed experiencing skin irritation. Healthcare personnel are susceptible to skin irritation due to their exposure to a variety of chemicals used in hospitals, including cleaning agents/products, disinfectants and sterilising solutions. When chemicals come into contact with the skin or when they are inhaled they result in allergic reactions [ 33 ]. Stress, exhaustion, anxiety, sleeplessness, chronic fatigue and blood pressure are among the psychosocial dangers that healthcare staff at Muvonde hospital and Driefontein Sanatorium hospital encounter. The study’s results support those of [ 3 , 21 ] that psychosocial hazards such as anxiety, stress, blood pressure and exhaustion can have an impact on healthcare professionals in Zimbabwe. The majority of healthcare professionals within the two rural hospitals specified that psychosocial risks cause them to experience stress. This could be caused by a number of factors, including stress inducing variables including workload, interpersonal issues and job uncertainty. Psychosocial risk has been linked to fatigue, which may be brought on by extended workdays and unfavourable working conditions.

Findings of the study indicated that work safety measures applied at Muvonde hospital and Driefontein Sanatorium hospital include safety inspection, safety training, monitoring worker’s safety behaviour, proper disposal of waste, provision of personal protective equipment and other measures such as screening workers for hepatitis B/C and tuberculosis. The study found that most healthcare employees at Muvonde and Driefontein Sanatorium hospitals stated use of PPE/C. This suggests that the two hospitals prioritise the use of PPE/C as a way for controlling occupational risks. In sub-Saharan Africa, hospitals prioritise the use of PPE/C and during the outbreak of Covid-19 pandemic they imported PPE/C to cover scarcity in healthcare facilities [ 32 ]. Healthcare professionals at Muvonde hospital and Driefontein Sanatorium hospital identified safety inspection as a risk management strategy. This is due to the fact that safety inspections are a useful tool for spotting possible risks and addressing them before they endanger patients, employees as well as visitors. Safety inspections assist in ensuring that appropriate infection control procedures are implemented.

Safety workshops and training have been recognised by healthcare professionals at Muvonde and Driefontein Sanatorium hospitals as crucial steps in reducing workplace risks. This is because healthcare professionals are subjected to a wide range of risks therefore, safety workshops and training give them the knowledge and abilities they need to recognise possible hazards, evaluate risks and put control measures in place. Healthcare personnel at Muvonde hospital and Driefontein Sanatorium hospital indicated that one institutional risk management strategy was the appropriate disposal of waste. Healthcare professionals may bring up waste disposal as a safety precaution because hospitals have stricter policies and procedures related to waste in place. Healthcare professionals stated appropriate waste management as a risk-reduction strategy because inappropriate hospital waste disposal can spread infectious diseases and seriously jeopardise public health. Proper waste storage of medical waste protect healthcare workers from risks associated with improper waste disposal [ 43 ].

Results of the study shows that most of the healthcare workers at Driefontein Sanatorium hospital and Muvonde hospital agree that there are safety policies within the hospitals. This indicates a high level of confidence in the hospital’s safety protocols and procedures. So the hospital’s commitment to ensuring the safety of everyone within its premises is commendable and should serve as an example to other healthcare facilities. The positive response from the questionnaire respondents could be due to the hospital’s strict adherence to regulatory requirements. This indicates that healthcare workers recognize the significance of having well-defined policies in place to ensure their safety while performing their duties. As a result the policies can create a culture of safety within the workplace, where employees are aware of the risks associated with their job and take proactive measures to mitigate them. According to [ 25 ] proper safety policies improves safety communication within the hospital departments and encourages hospital workers to protect patients and safeguard their own well-being.

The outcomes at Muvonde hospital and Driefontein Sanatorium hospital are consistent with each other since, when it came to work safety measures, most healthcare workers at both facilities rated them as good, very good, excellent with a minority indicating poor. Many factors may have contributed to the majority of respondents’ ratings of the institutional safety measures as good, very good and excellent. For example, hospitals may have spent money on staff training programmes to make sure they are aware of and adhere to appropriate safety procedures. Furthermore, hospitals might have conducted routine audits to find possible risks and hazards at work and take appropriate measures to address them. The small percentage of respondents who gave work safety analysis a low percentage rating might be the result of other staff members’ ignorance. Lastly, there might not be enough accountability or enforcement systems in place to guarantee that every employee follows the right safety procedures. This suggests that in order to guarantee that all factors are sufficiently addressed, there is space for improvement in hospital risk management procedures.

The results of the Chi-square test show a correlation between rating the effectiveness of work safety measures and work experience. This means that a health worker’s work experience affects how effective the measures used to manage risks are rated. This might be a result of a number of issues, including improved procedure knowledge, increased confidence in hospital employees’ abilities to recognise and reduce risks and increased awareness of the significance of workplace safety. Furthermore, workers with greater work experience might have witnessed more accidents or incidents at work, which might have improved their awareness of safety precautions that reduce risks. The results are in line with [ 16 ] who show that worker’s experience has a significant impact on how effective safety measures are rated for managing losses.

Conclusion and recommendations

The study indicated healthcare workers in hospitals of Chirumanzu district are affected with work-related risks as a result of inadequate equipment, poor training, negative safety behaviour, poor management and pressure due to high workload. As a result of this, the study has pinpointed areas where these hospitals’ work safety analysis performance needs to improve, highlighting the necessity of through risk assessments, the use of safety procedures and employee training to reduce possible dangers. Furthermore, the study has emphasised how important it is to promote a safety and accountability oriented culture in these healthcare environments. The research’s conclusions allow the formulation of the following recommendations to improve the effectiveness of work safety analysis in Chirumanzu District’s rural hospitals: To identify possible risks and weaknesses in the workplace, hospital management must perform comprehensive risk assessments. Assessment should include the tools and physical infrastructure as well as human factors. Additionally, healthcare facilities should develop and implement strict safety policies and procedures to manage hazards that have been identified. This covers procedures for managing hazardous items, preventing infections and responding to emergencies. Moreover, staff training and education should be improved. Regular training on work safety methods and procedures should be provided to healthcare staff. This will enable them to identify any hazards and take appropriate action. Hospital administration should place a high priority on fostering a culture of safety among employees by fostering open dialogue about safety issues and encouraging responsibility at all levels. In hospitals work safety efforts should receive sufficient funding, which should go towards purchasing safety gear, improving infrastructure and continuing training courses.

Data availability

The data generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Shabani, T., Jerie, S. & Shabani, T. Assessment of work safety analysis performance among rural hospitals of Chirumanzu district of midlands province, Zimbabwe. BMC Health Serv Res 24 , 938 (2024). https://doi.org/10.1186/s12913-024-11425-x

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DOI : https://doi.org/10.1186/s12913-024-11425-x

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