Silent Witnesses: Faculty Reluctance to Report Medical Students’ Professionalism Lapses
Bibliographic record
Abstract
PURPOSE: Assessing students' professionalism is a critical component of medical education. Nonetheless, faculty reluctance to report professionalism lapses remains a significant barrier to the effective identification, management, and remediation of such lapses. The authors gathered information from faculty who supervise medical students to better understand their perceived barriers to reporting. METHOD: In 2015-2016, data were collected using a group concept mapping methodology, which is an innovative, asynchronous, structured mixed-methods approach using qualitative and quantitative measures to identify themes characterizing faculty reluctance to report professionalism lapses. Participants from four U.S. and Canadian medical schools brainstormed, sorted, and rated statements about perceived barriers to reporting. Multidimensional scaling and hierarchical cluster analyses were used to analyze these data. RESULTS: Of 431 physicians invited, 184 con-tributed to the brainstorming task (42.7%), 48 completed the sorting task (11.1%), and 83 completed the rating task (19.3%). Participants identified six barriers or themes to reporting lapses. The themes "uncertainty about the process," "ambiguity about the 'facts,'" "effects on the learner," and "time constraints" were rated highest as perceived barriers. Demographic subgroup analysis by gender, years of experience supervising medical students, years since graduation, and practice discipline revealed no significant differences (P > .05). CONCLUSIONS: The decision to report medical students' professionalism lapses is more complex and nuanced than a binary choice to report or not. Faculty face challenges at the systems level and individual level. The themes identified in this study can be used for faculty development and to improve processes for reporting students' professionalism lapses.
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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 | 0.020 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".