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Record W2156203960 · doi:10.12927/hcpap.2008.19792

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

2008· article· en· W2156203960 on OpenAlexvenueaboutno aff
Jeremiah Hurley, Dianna Pasic, John N. Lavis, Anthony J. Culyer, Cameron Mustard, William Gnam

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPaymentGovernment (linguistics)IncentiveHealth careFinancial compensationFinanceCompensation (psychology)Economic growthEconomics

Abstract

fetched live from OpenAlex

Canada's workers' compensation boards (WCBs) finance healthcare for injured and ill workers in parallel with provincial health insurance plans. Parallel systems of healthcare 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 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)0.007
metaresearch head score (Gemma)0.032
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 score0.967
Threshold uncertainty score0.404

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.005
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.000

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 head0.093
GPT teacher head0.300
Teacher spread0.207 · 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

Citations13
Published2008
Admission routes2
Has abstractyes

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