Attitudes Towards Physicians Requiring Remediation: One-of-Us or Not-Like-Us?
Bibliographic record
Abstract
PURPOSE: The data for this paper were collected as part of a larger project exploring how the medical profession conceptualizes the task of supporting physicians struggling with clinical competency issues. In this paper, the authors focus on a topic that has been absent in the literature thus far-how physicians requiring remediation are perceived by those responsible for organizing remediation and by their peers in general. METHOD: Using a constructivist grounded theory approach, the authors conducted semistructured interviews with 17 remediation stakeholders across Canada. Given that in Canada health is a provincial responsibility, the authors purposively sampled stakeholders from across provincial and language borders and across the full range of organizations that could be considered as participating in the remediation of practicing physicians. RESULTS: Interviewees expressed mixed, sometimes contradictory, emotions toward and perceptions of physicians requiring remediation. They also noted that their colleagues, including physicians in training, were not always sympathetic to their struggling peers. CONCLUSIONS: The medical profession's attitude toward those who struggle with clinical competency-as individuals and as a whole-is ambivalent at best. This ambivalence grows out of psychological and cultural factors and may be an undiscussed factor in the profession's struggle to deal adequately with underperforming members. To contend with the challenge of remediating practicing physicians, the profession needs to address this ambivalence and its underlying causes.
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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 | 0.009 | 0.036 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".