What Role Should Resistance Play in Training Health Professionals?
Bibliographic record
Abstract
The role that resistance plays in medicine and medical education is ill-defined. Although physicians and students have been involved in protests related to the COVID-19 pandemic, structural racism, police brutality, and gender inequity, resistance has not been prominent in medical education's discourses, and medical education has not supported students' role and responsibility in developing professional approaches to resistance. While learners should not pick and choose what aspects of medical education they engage with, neither should their moral agency and integrity be compromised. To that end, the authors argue for professional resistance to become a part of medical education. This article sets out a rationale for a more explicit and critical recognition of the role of resistance in medical education by exploring its conceptual basis, its place both in training and practice, and the ways in which medical education might more actively embrace and situate resistance as a core aspect of professional practice. The authors suggest different strategies that medical educators can employ to embrace resistance in medical education and propose a set of principles for resistance in medicine and medical education. Embracing resistance as part of medical education requires a shift in attention away from training physicians solely to replicate and sustain existing systems and practices and toward developing their ability and responsibility to resist situations, structures, and acts that are oppressive, harmful, or unjust.
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 | 0.055 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".