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A Mixed-Methods Approach to Investigating Medication Errors

By: , & Published: 2020 | Product: SAGE Research Methods Cases: Medicine and Health
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The researchers used a sequential explanatory mixed-methods design (follow-up variant) to investigate medication errors from the perspectives of medical, nursing, anesthesiology, and pharmacy personnel. This study was conducted in Botswana with a sample of 130 participants providing the data. Study sites represented major geographical regions of the country, peri-urban and rural localities, public and private health facilities (hospitals, clinics, and healthposts) at varying levels of the complexity of services provided. The researchers used purposive sampling to select regions, localities, and health facilities; they used random sampling to select participants for the quantitative component of the study, and purposive sampling to select participants for the qualitative component. Sampling for the qualitative components was from among those who had participated in the quantitative part. Reason’s framework for accidents prevention provided the organizing framework for the study. A structured questionnaire was used to collect data for the quantitative component, whereas a semi-structured interview guide was used for the qualitative component. Descriptive statistics, cross-tabulations, and bivariate correlations were used for the quantitative data analysis, whereas content analysis was used for qualitative data. The findings from both quantitative and qualitative components were integrated to arrive at a comprehensive conclusion about the study findings. What the researchers learned is that the use of mixed-methods requires flexibility so as to accommodate peculiarities of a given context. Planning must consider delays inherent in multiple phases of data collection. The researchers recommend submitting a proposal with fully developed quantitative and qualitative protocols so as to avoid possible delays in ethical approval and subsequent loss to follow-up of participants.

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Quantitative data collection

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