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Record W2325675435 · doi:10.1097/jom.0b013e31827827fa

Financial Incentives of Experience Rating in Workers' Compensation

2013· article· en· W2325675435 on OpenAlexaff
Emile Tompa, Sheilah Hogg‐Johnson, Benjamin C. Amick, Ying Wang, Shen Enqing, Cam Mustard, Lynda S. Robson

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsInstitute for Work & HealthWorkplace Safety & Insurance BoardMcMaster UniversityNova Scotia Department of AgricultureUniversity of Toronto
Fundersnot available
KeywordsIncentiveWorkers' compensationCompensation (psychology)Occupational safety and healthRating systemMedicineBusinessActuarial scienceFinancePsychologyEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the incentive for primary and secondary prevention associated with experience rating in a retrospective workers' compensation program. METHODS: Panel data on 21,558 firms from 1998 to 2007 were used to estimate the relationship between the degree of experience rating and seven measures of workplace occupational health and safety outcomes. We focused on the impact of a policy change in 2004 in which the degree of experience rating was substantially increased for all firms. RESULTS: The 2004 increase in experience rating was associated with a reduction in the total, lost-time, no-lost-time, benefit days, permanent impairment, musculoskeletal disorder, and acute trauma claim rates. These observed changes follow secular trends. CONCLUSION: The association of experience rating with some claim outcomes and not others in some time periods suggests that firms may focus on claims and cost management practices.

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)0.009
metaresearch head score (Gemma)0.059
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 score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.030
GPT teacher head0.366
Teacher spread0.336 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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