Medical Education on Provider-Patient Power Dynamics: A Review of the Literature on Training for Responding to Patient Reports of Physician Misconduct
Bibliographic record
Abstract
Objective: Patients’ ability to report physician misconduct is essential for accountability, patient safety, and ethical healthcare delivery. However, entrenched power imbalances between providers and patients (reinforced by medical hierarchies, hidden curricula, and inconsistent institutional support) often hinder open dialog and ethical responsiveness. Medical trainees are frequently ill equipped to navigate these complex dynamics, particularly when confronted with patient complaints or observed misconduct. This review synthesizes the literature on how undergraduate and postgraduate medical education addresses provider–patient power dynamics, with a specific focus on preparing students to respond ethically and effectively to patient reports of physician misconduct. Methods: A structured literature search was conducted across PubMed, MEDLINE, and ERIC via terms related to medical education, power dynamics, physician‒patient relationships, misconduct, hidden curricula, and patient‒centered care. The inclusion criteria focused on peer-reviewed studies from 2000–2024 that addressed educational content related to professionalism, ethical training, communication, and error disclosure in UME or PGME settings. Among the 1,269 records identified, 29 met the inclusion criteria and were synthesized thematically. Results: Three overarching themes emerged: (1) structural and cultural barriers to addressing power—including the hidden curriculum, hierarchical silencing, and systemic inequities; (2) emotional and ethical learning—highlighting the need for curricula in emotional intelligence, moral courage, and reflective practice; and (3) curricular gaps and interventions—identifying promising but fragmented efforts such as boundary education, empathy training, and error disclosure programs. However, these initiatives are often inconsistently applied and insufficiently integrated into core curricula. Conclusion: Medical education insufficiently prepares learners to manage provider–patient power imbalances and respond to patient complaints of physician misconduct. Addressing this gap requires coordinated reforms that embed emotional and ethical competencies, structural competency, and patient-centered communication throughout training. Without such reform, future physicians risk perpetuating a culture of silence, undermining trust, and failing to meet the ethical demands of contemporary medical practice.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".