Bibliographic record
Abstract
OBJECTIVE To explore the boundaries and overlap of practice profiles of primary care physicians (PCPs), including FPs and GPs, and community medicine specialists (CMs), particularly in the area of community-oriented clinical care. DESIGN Analysis of data from the 2004 National Physician Survey. Analyses included frequencies, cross-tabulations, and χ2 statistics. SETTING Canada. PARTICIPANTS Primary care physicians and CMs who responded to the 2004 National Physician Survey. MAIN OUTCOME MEASURES For PCPs and CMs, we compared main work and patient care settings, areas of professional activity, and credentials to practise public health or family medicine. Among CMs, we examined the most commonly treated conditions and services provided for evidence of community-oriented clinical care. RESULTS Data were available for 154 CMs and 11 041 PCPs. The most common work setting for CMs was government or public health agencies, while for PCPs it was offices, clinics, or community care settings, including community hospitals. Among CMs, 59.7% indicated that community medicine or public health practice was an area of professional activity and 13.0% indicated that they participated in primary care. The corresponding proportions for PCPs were 15.3% and 78.2%, respectively. Generally, CMs engaged in a mixture of individual-level and population-level practice activities, although the former was not distinguished by increased clinical prevention, health promotion, or disease prevention services. Of CMs who indicated that primary care was an area of professional activity, 55.0% had the relevant credentials, compared with only 1.9% of PCPs who conversely indicated that community medicine or public health was an area of professional activity. CONCLUSION In Canada CMs and PCPs have distinct practice profiles, despite some overlaps. Further role and practice profile refinement for both physician groups has implications for training, credentialing, and deployment within the health care system.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".