Who delivers preventive care as recommended?: Analysis of physician and practice characteristics.
Bibliographic record
Abstract
ABSTRACTOBJECTIVETo ascertain which physician and practice characteristics are associated with self-reported provision of preventive care as recommended by the Canadian Task Force on Preventive Health Care.DESIGNCross-sectional analysis of data from a decennial survey.SETTINGSouthwestern Ontario.PARTICIPANTSA total of 731 family physicians in various practice settings.MAIN OUTCOME MEASURESNumber of patients to whom these physicians provided the recommended preventive services based on physicians' responses to various scenarios presented in the survey. The responses were scored, and the median score was used to dichotomize physicians into high- and low-scoring groups.RESULTSClose to two-thirds of the physicians (61%) were in the high-scoring group. Female family physicians, graduates of Canadian medical schools, and physicians whose practices were organized into family health teams, family health groups, family health networks, community health centres, or health services organizations were more likely to be in the high-scoring group. Physicians practising solo and international medical graduates were more likely to be in the low-scoring group.CONCLUSIONReorganizing delivery of primary care into group practice models might improve provision of preventive services. Licensing requirements for international medical graduates should ensure that these physicians are adequately trained to provide preventive services as recommended in the Canadian context. More research is needed before our results can be generalized beyond southwestern Ontario.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".