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Record W4417520302 · doi:10.1177/15394492251397844

Analyzing British Columbia Workers Compensation Occupational Therapy Usage With Public Safety Personnel

2025· article· en· W4417520302 on OpenAlexaffabout
Megan Edgelow, Kayla Beren

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsRehabilitationCompensation (psychology)Occupational injuryOccupational safety and healthOccupational therapyWorkers' compensationOccupational rehabilitationDescriptive statistics

Abstract

fetched live from OpenAlex

Public safety personnel (PSP) have higher rates of work-related psychological injuries compared to the general public. Occupational therapists frequently provide rehabilitation and return to work (RTW) services for PSP. In order to understand the usage of occupational therapy (OT) treatment with psychologically injured PSP from British Columbia, Canada, a descriptive quantitative approach was used to explore summary data provided by WorkSafeBC (WSBC) for psychologically injured PSP who had an accepted mental disorder injury claim between 2019 and 2023, comparing those who received community OT treatment and those who did not. The data revealed that PSP with greater injury severity and longer lengths of time away from work more frequently received OT treatment as part of their WSBC mental disorder injury claim; it is possible that this higher degree of claim complexity influenced their RTW outcomes. Workers' compensation organizations should review their RTW processes to ensure prompt access to relevant treatment and supports, including occupational therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.254
GPT teacher head0.516
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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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