Interpretive description: A flexible qualitative methodology for medical education research
Bibliographic record
Abstract
BACKGROUND: Qualitative research approaches are increasingly integrated into medical education research to answer relevant questions that quantitative methodologies cannot accommodate. However, researchers have found that traditional qualitative methodological approaches reflect the foundations and objectives of disciplines whose aims are recognizably different from the medical education domain of inquiry (Thorne, 2016, Interpretive description. New York, NY: Routledge). Interpretive description (ID), a widely used qualitative research method within nursing, offers an accessible and theoretically flexible approach to analysing qualitative data within medical education research. ID is an appropriate methodological alternative for medical education research, as it can address complex experiential questions while producing practical outcomes. It allows for the advancement of knowledge surrounding educational experience without sacrificing methodological integrity that long-established qualitative approaches provide. PURPOSE: In this paper, we present interpretive description as a useful research methodology for qualitative approaches within medical education. We then provide a toolkit for medical education researchers interested in incorporating interpretive description into their study design. We propose a coherent set of strategies for identifying analytical frameworks, sampling, data collection, analysis, rigour and the limitations of ID for medical education research. We conclude by advocating for the interpretive description approach as a viable and flexible methodology for medical education research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.276 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".