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Record W3121479658

Parallel Payers and Preferred Access: How Canada's Workers' Compensation Boards Expedite Care for Injured and Ill Workers

2007· preprint· en· W3121479658 on OpenAlexaboutno aff
Jeremiah Hurley, Dianna Pasic, John N. Lavis, Anthony J. Culyer, Cameron Mustard, William Gnam

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPaymentGovernment (linguistics)IncentiveFinanceHealth careFinancial compensationCompensation (psychology)Economic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

Canada’s Workers’ Compensation Boards (WCBs) finance health care for injured and ill workers in parallel to provincial health insurance plans. Parallel systems of health care finance can create preferred access for some. WCBs have in recent years pursued a number of strategies to expedite or improve the quality of care for injured or ill workers, including in-house provision in WCB-owned facilities, contracting with private, for-profit clinics, contracting with publicly funded hospitals and clinics for use of facilities “off-hours”, and supporting specialized clinics within publicly funded hospitals. Many of these strategies incorporate incentive payments to physicians and facilities for treating WCB cases more quickly than patients covered by provincial plans. In this paper we both document the development of these strategies and discuss their implications for physicians, patients, government, and the provincial public insurance plans.

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)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.490

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.000

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 head0.084
GPT teacher head0.346
Teacher spread0.262 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

Explore more

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