Qualitative Research in Medical Education
Bibliographic record
Abstract
Qualitative research encompasses multiple research methodologies, including ethnography, grounded theory, case study, phenomenology, narrative inquiry, action research, and discourse analysis. Qualitative research studies are carried out through a set of tools for data collection and analysis. This chapter presents methods for data collection and approaches to data analysis. The process of the researcher making meaning of what s/he is seeing should involve clarifications with participants and connections with extant theory, and should not be considered inaccurate if done with attention to rigour and reflexivity. Medical education researchers commonly conduct analysis using teams of researchers. The purpose of involving more than one individual in the analysis varies, and depends on the epistemological stance of the work. Researchers need to be both thoughtful and transparent about their purposes and procedures with regard to theory building and theory use, in order to advance understanding of medical education through rigorous qualitative 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 | 0.124 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".