Research in Medical Education: Balancing Service and Science*
Bibliographic record
Abstract
Since the latter part of the 1990's, the English-speaking medical education community has been engaged in a debate concerning the types of research that should have priority. To shed light on this debate and to better understand its implications for the practice of research, 23 semi-structured interviews were conducted with "influential figures" from the community. The results were analyzed using the concept of "field" developed by the sociologist Pierre Bourdieu. The results reveal that a large majority of these influential figures believe that research in medical education continues to be of insufficient quality despite the progress that has taken place over the past 2 decades. According to this group, studies tend to be both redundant and opportunistic, and researchers tend to have limited understanding of both theory and methodological practice from the social sciences. Three factors were identified by the participants to explain the current problems in research: the working conditions of researchers, budgetary restraints in financing research in medical education, and the conception of research in the medical environment. Two principal means for improving research are presented: intensifying collaboration between PhD's and clinicians, and encouraging the diversification of perspectives brought to bear on research in medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.326 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.019 | 0.148 |
| Scholarly communication | 0.043 | 0.035 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".