How Do Medical Schools Identify and Remediate Professionalism Lapses in Medical Students? A Study of U.S. and Canadian Medical Schools
Bibliographic record
Abstract
PURPOSE: Teaching and assessing professionalism is an essential element of medical education, mandated by accrediting bodies. Responding to a call for comprehensive research on remediation of student professionalism lapses, the authors explored current medical school policies and practices. METHOD: In 2012-2013, key administrators at U.S. and Canadian medical schools accredited by the Liaison Committee on Medical Education were interviewed via telephone or e-mail. The structured interview questionnaire contained open-ended and closed questions about practices for monitoring student professionalism, strategies for remediating lapses, and strengths and limitations of current systems. The authors employed a mixed-methods approach, using descriptive statistics and qualitative analysis based on grounded theory. RESULTS: Ninety-three (60.8%) of 153 eligible schools participated. Most (74/93; 79.6%) had specific policies and processes regarding professionalism lapses. Student affairs deans and course/clerkship directors were typically responsible for remediation oversight. Approaches for identifying lapses included incident-based reporting and routine student evaluations. The most common remediation strategies reported by schools that had remediated lapses were mandated mental health evaluation (74/90; 82.2%), remediation assignments (66/90; 73.3%), and professionalism mentoring (66/90; 73.3%). System strengths included catching minor offenses early, emphasizing professionalism schoolwide, focusing on helping rather than punishing students, and assuring transparency and good communication. System weaknesses included reluctance to report (by students and faculty), lack of faculty training, unclear policies, and ineffective remediation. In addition, considerable variability in feedforward processes existed between schools. CONCLUSIONS: The identified strengths can be used in developing best practices until studies of the strategies' effectiveness are conducted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".