Seven ways to get a grip on using participant observation in medical education research
Bibliographic record
Abstract
Medical education phenomena are complex, and researchers need to use diverse methods to explore topics. Participant observation is a qualitative research method that connects researchers to human interactions, allowing them to experience firsthand behaviours, conversations, characteristics, and qualities related to the phenomenon under study. It can provide unique insights, beyond those of participant narratives, and enhance understanding. However, this method is rarely used in medical education research; it is challenging and resource-intensive to implement, which likely discourages researchers from using it. To help researchers get a grip on using it in medical education research, we offer seven recommendations for planning participant observation: Determine the study setting(s), Identify key interest-holders and establish relationships, Determine the researcher-participant relationship to be established, Take steps to minimize reactivity to research, Use knowledge to guide data collection procedures, Use knowledge to inform instrument development, and Anticipate possible dilemmas and be mindful of unanticipated ones. We urge researchers to consider participant observation when appropriate to advance methods in 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 | 0.790 | 0.712 |
| Meta-epidemiology (narrow) | 0.008 | 0.010 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.031 | 0.142 |
| Scholarly communication | 0.051 | 0.098 |
| Open science | 0.016 | 0.060 |
| Research integrity | 0.037 | 0.055 |
| Insufficient payload (model declined to judge) | 0.006 | 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".