Financial Incentives and Cervical Cancer Screening Participation in Ontario’s Primary Care Practice Models
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this paper is to compare cervical cancer screening rates of family physicians in Ontario's two dominant reformed practice models, Family Health Group (FHG) and Family Health Organization (FHO), and traditional fee-for-service (FFS) model. Both reformed models formally enrol patients and offer extensive pay-for-performance incentives; however, they differ by remuneration for core services (FHG is FFS; FHO is capitated). The secondary objective is to estimate the average and marginal costs of screening in each model. METHODS: Using administrative data on 7,298 family physicians and their 2,083,633 female patients aged 35-69 eligible for cervical cancer screening in 2011, we assessed screening rates after adjusting for patient and physician characteristics. Predicted screening rates, fees and bonus payments were used to estimate the average and marginal costs of cervical cancer screening. RESULTS: Adjusted screening rates were highest in the FHG (81.9%), followed by the FHO (79.6%), and then the traditional FFS model (74.2%). The cost of a cervical cancer screening was $18.30 in the FFS model. The estimated average cost of screening in the FHGs and FHOs were $29.71 and $35.02, respectively, while the corresponding marginal costs were $33.05 and $39.06. DISCUSSION: We found significant differences in cervical cancer screening rates across Ontario's primary care practice models. Cervical screening rates were significantly higher in practice models eligible for incentives (FHGs and FHOs) than the traditional FFS model. However, the average and marginal cost of screening were lowest in the traditional FFS model and highest in the FHOs.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".