After-Hours Incentives\nand Emergency Department\nVisits: Evidence from Ontario
Bibliographic record
Abstract
Un aspect important de la réforme des soins de santé primaires en Ontario est d’inciter les médecins à effectuer des heures supplémentaires pour améliorer l’accès aux principaux services de soins primaires et pour réduire le nombre des visites aux urgences dans les hôpitaux. Les données empiriques relatives à ce lien sont ambiguës. Les auteurs tentent d’expliquer cette ambiguïté et se servent de riches données administratives ontariennes pour analyser attentivement si les mesures d’encouragement aux heures supplémentaires infl uent sur le recours aux services d’urgence et pourquoi. Les données étudiées portent sur les visites aux cabinets des médecins et les visites aux urgences de 2003 à 2007, période marquée par des changements exogènes dans les mesures d’encouragement aux heures supplémentaires. Selon les auteurs, tout porte à croire que ces mesures d’encouragement contribuent à diminuer les visites moins urgentes en salles d’urgence. Abstract: One important component of primary care reform in Ontario is to incentivize physicians to work after hours to improve access to core primary care services and potentially reduce visits to hospital emergency departments (EDs). Empirically, evidence on this link is ambiguous. We suggest reasons for this ambiguity and then harness rich administrative data from Ontario to carefully investigate whether and why after-hours incentives affect ED usage. The data cover physicians’ office visits and ED visits from 2003 to 2007, a period with exogenous changes in after-hours incentives. We find strong evidence that less urgent ED visits are reduced as a result of these incentives.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".