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Record W1846614335 · doi:10.1177/001979390706100107

The Impact of Provider Choice on Workers' Compensation Costs and Outcomes

2005· article· en· W1846614335 on OpenAlexaff
David Neumark, Peter S. Barth, Richard B. Victor

Bibliographic record

VenueIndustrial and Labor Relations Review · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsWorkers Compensation Board of British Columbia
Fundersnot available
KeywordsWorkers' compensationLimitingCompensation (psychology)BusinessWork (physics)Actuarial scienceLabour economicsDemographic economicsEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Using survey data collected in 2002 and 2003 in California, Massachusetts, Pennsylvania, and Texas on workers injured 3 to 3.5 years earlier, coupled with information on the associated workers' compensation claims from the Workers Compensation Research Institute, the authors examine how provider choice in workers' compensation is related to costs and to workers' outcomes. They find that employee choice of the provider, by comparison with employer choice, was associated with higher costs and worse return-to-work outcomes. Although the same rate of physical recovery was found for both groups, workers who chose their providers reported higher satisfaction with medical care. The higher costs and worse return-to-work outcomes associated with employee choice arose largely when employees selected a new provider, rather than a provider with whom they had a pre-existing relationship. The findings lend some support to recent policy changes limiting workers' ability to choose a provider with whom they do not have a prior relationship.

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)0.006
metaresearch head score (Gemma)0.034
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 score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.345
Teacher spread0.247 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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