“Those Darn Kids”: Having Meaningful Conversations about Learner Resistance in Medical Education
Bibliographic record
Abstract
describes the principles professionals should follow when they seek to counter social harm and injustice. Applied to medical education, the principles of professional resistance can help learners and teachers balance the responsibilities to respond to harm and injustice with their roles and responsibilities as health professionals. However, there remains the problem of how educators and leaders can constructively respond to learner acts of resistance. It would seem that many leaders have dismissed learner resistance with variations on "Those Darn Kids!", a complaint that has long been levied at those in younger generations who challenge power and authority. How can productive change in medical education be achieved if learners' complaints are not taken seriously? Rather than dismissal, leaders and educators in these situations need the tools to engage learners in conversations that draw out their concerns.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".