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Record W3210208029 · doi:10.1136/oem-2021-epi.152

O-357 Examining variations in work disability duration by firm size: a comparative study of workers’ compensation claims in Canada and Australia

2021· article· en· W3210208029 on OpenAlexaffabout
Robert Macpherson, Tyler Lane, Alex Collie, Chris McLeod

Bibliographic record

VenueOral Presentations · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPercentileReceiptDuration (music)Work (physics)Quantile regressionWorkers' compensationDemographic economicsMedicineBusinessCompensation (psychology)DemographyPsychologyEconomicsAccountingStatisticsEngineeringEconometrics

Abstract

fetched live from OpenAlex

Introduction Small firms, while more numerous than large firms, often face greater challenges in implementing effective occupational health and safety and return-to-work programs. Research has rarely looked at firm size as a determinant of work disability duration and has been limited to single jurisdictions. Objectives To identify whether there were differences in work disability duration between injured workers employed by small, medium and large firms and whether these differences varied between workers’ compensation jurisdictions in Canada (CAN) and Australia (AUS). Methods Workers’ compensation data were used to identify comparable lost-time, work-related injury and musculoskeletal disorder claims in five Canadian and five Australian jurisdictions between 2011 and 2015. Work disability duration was measured using cumulative days in receipt of disability benefit payments up to one-year post-injury. Jurisdiction-specific quantile regression models were used to compare cumulative disability days paid from small ( < 2 0 full-time equivalents (FTEs), medium (20–199 FTEs), large (200+ FTEs) firms at 25th, 50th, and 75th percentiles in the disability distribution, adjusting for confounders. Results Differences in work disability duration by firm size were observed in all jurisdictions except the Northern Territories (AUS). Compared to large firms, small firms were paid the most disability days at each percentile, particunarly in Victoria (AUS), Saskatchewan (CAN), the Australian Capital Territory, and Tasmania (AUS), where an additional 63.0, 31.1, 37.0, and 27.4 days were paid at the 75th percentiles of the distributions, respectively. Claims from medium-sized firms were generally paid more disability days than large firms except in Western Australia and Tasmania, where they were paid less. Conclusions Small firms were shown to have the longest work disability durations in 9 of the 10 study jurisdictions. Claims management processes need to be sensitive to the challenges that small firms face in accommodating and returning injured workers back to work.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.024
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.247
GPT teacher head0.506
Teacher spread0.259 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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