Physician-patient encounters: The structure of performance in family and general office practice
Bibliographic record
Abstract
INTRODUCTION: The College of Physicians and Surgeons of Ontario, the regulatory authority for physicians in Ontario, Canada, conducts peer assessments of physicians' practices as part of a broad quality assurance program. Outcomes are summarized as a single score and there is no differentiation between performance in various aspects of care. In this study we test the hypothesis that physician performance is multidimensional and that dimensions can be defined in terms of physician-patient encounters. METHODS: Peer assessment data from 532 randomly selected family practitioners were analyzed using factor analysis to assess the dimensional structure of performance. Content validity was confirmed through consultation sessions with 130 physicians. Multiple-item measures were constructed for each dimension and reliability calculated. Analysis of variance determined the extent to which multiple-item measure scores would vary across peer assessment outcomes. RESULTS: Six performance dimensions were confirmed: acute care, chronic conditions, continuity of care and referrals, well care and health maintenance, psychosocial care, and patient records. DISCUSSION: Physician performance is multidimensional, including types of physician-patient encounters and variation across dimensions, as demonstrated by individual practice. A conceptual framework for multidimensional performance may inform the design of meaningful evaluation and educational recommendations to meet the individual performance of practicing physicians.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".