The anatomy and physiology of conflict in medical education: a doorway to diagnosing the health of medical education systems
Bibliographic record
Abstract
This qualitative study uses data from students, teachers and administrators to deepen our understanding of conflict in medical education, its nature and its consequences. It especially looks at systemic issues which may foster or hinder the health of an educational system or of any organization. Its intention is to provide better understanding of the medical education system so that this knowledge can be used to enhance the health of future medical education systems. It is preliminary to a study that would focus on ways of improving the healthiness of future systems. The findings underline the importance of moral education in the training of our future physicians (McWhinney, 1986). The importance of example by faculty and staff and moral development of the physician flows from the authors' data and their interpretation of its meaning. Also, it further underlines the importance of faculty and medical educators modeling both caring and exemplary moral behavior within our educational institutions. Bandura (1986) developed the notion of modeling and showed that, 'even at a preconscious level, we learn moral behaviors through observing and imitating authority figures and/or significant others' (Crysdale, 2006). This is especially important because caring, or compassionate presence, is so essential to healing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".