Factors affecting physician performance: implications for performance improvement and governance.
Bibliographic record
Abstract
BACKGROUND: A physician's personal and professional characteristics constitute only one, and not necessarily the most important, determining factor of clinical performance. Our study assessed how physician, organizational and systemic factors affect family physicians' performance. METHOD: Our study examined 532 family practitioners who were randomly selected for peer assessment by the College of Physicians and Surgeons of Ontario. A series of multivariate regression analyses examined the impact of physician factors (e.g., demographics, certification) on performance scores in five clinical areas: acute care, chronic conditions, continuity of care and referrals, well care and records. A second series of regressions examined the simultaneous effects of physician, organizational (e.g., practice volume, hours worked, solo practice) and systemic factors (e.g., northern practice location, community size, physician-to-population ratio). RESULTS: OUR STUDY HAD THREE KEY FINDINGS: (a) physician factors significantly influence performance but do not appear to be nearly as important as previously thought; (b) organizational and systemic factors have significant effects on performance after the effects of physician factors are controlled; and (c) physician, organizational and systemic factors have varying effects across different dimensions of clinical performance. CONCLUSIONS: We discuss the implications of our results for performance improvement and physician governance insofar as both need to consider the broader environmental context of medical practice.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".