Experiential knowledge of risk and support factors for physician performance in Canada: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: To identify, understand and explain potential risk and protective factors that may influence individual and physician group performance, by accessing the experiential knowledge of physician-assessors at three medical regulatory authorities (MRAs) in Canada. DESIGN: Qualitative analysis of physician-assessors' interview transcripts. Telephone or in-person interviews were audio-recorded on consent, and transcribed verbatim. Interview questions related to four topics: Definition/discussion of what makes a 'high-quality physician;' factors for individual physician performance; factors for group physician performance; and recommendations on how to support high-quality medical practice. A grounded-theory approach was used to analyse the data. SETTING: Three provinces (Alberta, Manitoba, Ontario) in Canada. PARTICIPANTS: Twenty-three (11 female, 12 male) physician-assessors from three MRAs in Canada (the College of Physicians & Surgeons of Alberta, the College of Physicians and Surgeons of Manitoba and the College of Physicians and Surgeons of Ontario). RESULTS: Participants outlined various protective factors for individual physician performance, including: being engaged in continuous quality improvement; having a support network of colleagues; working in a defined scope of practice; maintaining engagement in medicine; receiving regular feedback; and maintaining work-life balance. Individual risk factors included being money-oriented; having a high-volume practice; and practising in isolation. Group protective factors incorporated having regular communication among the group; effective collaboration; a shared philosophy of care; a diversity of physician perspectives; and appropriate practice management procedures. Group risk factors included: a lack of or ineffective communication/collaboration among the group; a group that doesn't empower change; or having one disruptive or 'risky' physician in the group. CONCLUSIONS: This is the first qualitative inquiry to explore the experiential knowledge of physician-assessors related to physician performance. By understanding the risk and support factors for both individual physicians and groups, MRAs will be better-equipped to tailor physician assessments and limited resources to support competence and enhance physician performance.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".