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Record W6998369881

After-Hours Incentives\nand Emergency Department\nVisits: Evidence from Ontario

2020· article· en· W6998369881 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary careIncentiveEmergency departmentHospital care
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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.

Opus teacher head0.031
GPT teacher head0.251
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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