Views of family physicians in southwestern Ontario on preventive care services and performance incentives
Bibliographic record
Abstract
Although the Canadian Task Force on Preventive Health Care recommends that several preventive care services be routinely provided to eligible patients in primary care settings, the delivery rate for these services continues to be suboptimal. A recent metaanalysis found evidence to support the use of several strategies to improve preventive care delivery rates, yet many practitioners still rely primarily on an opportunistic approach for delivering preventive care services. Primary care networks (PCN) and family health networks (FHN) are two new models of primary health care delivery in Ontario, characterized by patient rostering and a capitation payment structure with added incentives. One such incentive is the preventive care management program, offering annual preventive care performance bonuses that are calculated from the delivery rate of biennial Papanicolaou smear screening (age 35–69 years) and mammography screening (age 50– 69 years), annual influenza vaccination (age 65 years or over), and a series of five childhood immunizations by 2 years of age. The annual bonus payments increase incrementally to a maximum of $CAN 2200 for delivery rates ranging from 75% for mammography screening to 95% for childhood immunizations. Each physician is also eligible to claim a management fee of $CAN 6.86 per service for each overdue patient contacted by reminder letter and telephone call. The ‘Provider and Patient Reminders in Ontario: Multi-strategy Prevention Tools’ (P-PROMPT) demonstration project is currently implementing a reminder and recall strategy in participating PCN and FHN practices across southwestern Ontario to increase the delivery of the four targeted preventive care services. The purpose of the current report was to explore the views of family physicians enrolled in the P-PROMPT project on preventive care in general and of the preventive care management program in particular, and to gather information on their current preventive care delivery strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".