Association of physician financial incentives with primary care enrolment of adults with serious mental illnesses in Ontario: a retrospective observational population-based study
Bibliographic record
Abstract
BACKGROUND: Financial incentives may improve primary care access for adults with schizophrenia or bipolar disorder (serious mental illness [SMI]). We studied the association between receipt of the SMI financial premium paid to primary care physicians and rostering of adults with SMI in different patient enrolment models (PEMs), including enhanced fee-for-service and capitation-based models with and without interdisciplinary team-based care. METHODS: We conducted a retrospective cohort study involving Ontario adults (≥18 yr) with SMI in PEM practices, in fiscal years 2016/17 and 2017/18. Using negative binomial models, we examined relations between rostering and the primary care model and the contribution of the incentive. Similar models were developed for adults with type 1 or 2 diabetes mellitus and the general population. RESULTS: Among 9730 physicians in PEM practices, 4866 (50.0%) received a premium and 448 319 (88.4%) people with SMI in PEMs were rostered. Compared with enhanced fee for service, the likelihood of rostering people with SMI was 3.0% higher for patients in capitation with team-based care (adjusted relative risk [RR] 1.03, 95% confidence interval [CI] 1.02-1.04), with similar results for capitation without team-based care (adjusted RR 1.00 95% CI 0.99-1.01). Rostering for people with diabetes was similar in team-based care (adjusted RR 1.02, 95% CI 1.02-1.03) but higher in capitation without team-based care (adjusted RR 1.03, 95% CI 1.02-1.03) and slightly higher for the Ontario population (team-based care 1.04, 95% CI 1.04-1.05, capitation without team-based care 1.03, 95% CI 1.03-1.04). INTERPRETATION: Rostering of people with SMI was lower than for the general population. Additional policy measures are needed to address persisting inequities and to promote rostering of this underserved population with complex needs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".