Navigating Dilemmas Arising from Advocacy and Resistance in Medical Education and Medical Practice
Bibliographic record
Abstract
Background: Advocacy and resistance are undertheorized in medical education, yet trainees are often encouraged by their teachers to engage in these activities as a way of helping patients, mitigating healthcare system weaknesses, or challenging harms or injustices. How health professionals can and should engage in advocacy or resistance (which can be treated as a dyad of advocacy-resistance) is undertheorized, which can create confusion for trainees and lead to harms. Although acts of advocacy-resistance are often framed as pro-social, applications of advocacy-resistance can create inequity in seeking to reduce it, and they can create challenges for those trying to negotiate this perilous landscape. Method: The authors respond to the need for a more robust theoretical grounding in this space by taking a dialogical approach (based on abductive group discussion and debate, reading and rereading the literature, and collaborative writing and theory building) to explore ethical dilemmas that can arise from healthcare practitioner and trainee engagement in acts of advocacy-resistance. Findings: Four broad dilemmas arising from healthcare practitioner and trainee acts of advocacy-resistance are described: where the loci of responsibilities lie, how professional identity and agency are situated within a collective, balancing competing needs and priorities, and managing harm that can result from engaging in advocacy-resistance. The authors describe contributing factors including equity, identity, needs, priorities, responsibilities, and the advocacy-resistance dyad itself. Conclusions: In better understanding the dilemmas that acts of advocacy-resistance can create, healthcare providers, educators, and trainees should be better able to negotiate this complex and yet necessary space.
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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 | 0.246 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.053 | 0.184 |
| Scholarly communication | 0.046 | 0.041 |
| Open science | 0.009 | 0.053 |
| Research integrity | 0.031 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 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".