Bibliographic record
Abstract
To the Editor: When physicians encounter systemic injustice and moral harm in their learning and clinical environments, they can either accept the system as it is and accommodate it, or they can seek to challenge the system through intentional acts of professional resistance.1 McCleary-Gaddy and Mancias’ AM Last Page2 described various strategies leaders may enact to support social justice activism in their medical schools. Like our work on resistance,1,3 their infographic recognizes that trainees experience harm and injustice in their clinical and training environments and are in need of support as they challenge the system. However, their argument assumes that trainees are willing to share their concerns with leadership. We have found that this is not always the case. Frequently, trainees are engaged in dissent long before their conversations surface with those in power. Therefore, activism is only one expression of professional resistance.3 There are more covert strategies, such as noncompliance, nonconformity, and debate,4 that can also play a valuable part in professional resistance.1 In our work, we have defined “resistance” as “Individual and collective expressions of condemnation of social harms and injustices, with the intent of stopping them, preventing them from recurring, and/or holding those responsible to account.”1 Given the breadth of approaches to resistance, we ask medical educators to be mindful of the many ways that trainees may be legitimately challenging injustices. Sometimes this resistance may not be explicit or visible due to fears that those resisting may be punished or excluded. If those in medical education are to tackle systemic harms and injustices, we need a principled and theoretically grounded foundation for this work that supports those engaged in resistance while maintaining professional legitimacy and accountability. The more vulnerable those involved in resistance are, the more they need this foundation. To that end, we have questioned the absence of a coherent discourse on resistance in medical education, provided a framework to guide physicians’ individual and collective actions,1 and compared/contrasted professional resistance with neighboring concepts, such as advocacy.3 We welcome McCleary-Gaddy and Mancias and others to join this conversation and advance principles of professional resistance.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".