Payment incentives for community-based psychiatric care in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: In September 2011, the government of Ontario implemented payment incentives to encourage the delivery of community-based psychiatric care to patients after discharge from a psychiatric hospital admission and to those with a recent suicide attempt. We evaluated whether these incentives affected supply of psychiatric services and access to care. METHODS: We used administrative data to capture monthly observations for all psychiatrists who practised in Ontario between September 2009 and August 2014. We conducted interrupted time-series analyses of psychiatrist-level and patient-level data to evaluate whether the incentives affected the quantity of eligible outpatient services delivered and the likelihood of receiving follow-up care. RESULTS: Among 1921 psychiatrists evaluated, implementation of the incentive payments was not associated with increased provision of follow-up visits after discharge from a psychiatric hospital admission (mean change in visits per month per psychiatrist 0.0099, 95% confidence interval [CI] -0.0989 to 0.1206; change in trend 0.0032, 95% CI -0.0035 to 0.0095) or after a suicide attempt (mean change -0.0910, 95% CI -0.1885 to 0.0026; change in trend 0.0102, 95% CI 0.0045 to 0.0159). There was also no change in the probability that patients received follow-up care after discharge (change in level -0.0079, 95% CI -0.0223 to 0.0061; change in trend 0.0007, 95% CI -0.0003 to 0.0016) or after a suicide attempt (change in level 0.0074, 95% CI -0.0094 to 0.0366; change in trend 0.0006, 95% CI -0.0007 to 0.0022). INTERPRETATION: Our results suggest that implementation of the incentives did not increase access to follow-up care for patients after discharge from a psychiatric hospital admission or after a suicide attempt, and the incentives had no effect on supply of psychiatric services. Further research to guide design and implementation of more effective incentives is warranted.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".