Effects of practice setting on GPs' provision of care.
Bibliographic record
Abstract
OBJECTIVE: To define a physician classification system based on practice settings and to analyze the service provision associated with those classifications. DESIGN: A cross-sectional, retrospective study. SETTING: Province of Quebec. PARTICIPANTS: All GPs in Quebec in 2002 who had been practising for at least 2 years. MAIN OUTCOME MEASURES: Practice setting variables were based on physician income in the different settings. Service provision was assessed using indicators related to continuity, comprehensiveness, accessibility, and productivity of services provided by the GPs. A multiple correspondence analysis with ascending hierarchical classification was conducted to construct the taxonomy of GPs based on their practice settings. RESULTS: Our study produced 7 practice setting models. Two were essentially single-practice models. The 5 others combined several settings. Service provision varied from one model to another. Continuity was greater in the private practice model, in which older GPs were predominant, while accessibility was greater in multi-institutional practice models, in which younger GPs were more active. CONCLUSION: To ensure balance between continuity, accessibility, and comprehensiveness in primary care services provided by GPs, it is important to consider the service provision associated with different practice models.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".