Performance assessment. Family physicians in Montreal meet the mark!
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical performance of a representative non-volunteer sample of family physicians in metropolitan Montreal, Que. DESIGN: Assessment of clinical performance was based on inspection visits to offices, peer review of medical records, and chart-stimulated recall interviews. The procedure was the one usually followed by the Professional Inspection Committee of the Collège des médecins du Québec. SETTING: Family physicians' practices in metropolitan Montreal. PARTICIPANTS: One hundred randomly selected family physicians. INTERVENTIONS: For each physician, 30 randomly chosen patient charts with data on three to five previous visits were reviewed using explicit criteria and a standard scale using global scores from 1 to 5 (unacceptable to excellent). MAIN OUTCOME MEASURES: Scores were assigned for office practices; record keeping; number of continuing medical education (CME) activities; and quality of clinical performance assessed in terms of investigation plan, diagnostic accuracy, treatment plan, and relevance of care. RESULTS: Overall performance was judged to be good to excellent for 98% of physicians in their private practices; for 90% of physicians concerning CME activities; for 94% of physicians concerning their clinical performance in terms of quality of care; and for 75% of physicians as to record keeping. There was a link between record keeping and quality of care as well as between the number of CME activities and quality of care. CONCLUSION: The overall clinical performance of family physicians in the greater Montreal region is excellent.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".