Compensation patterns for healthcare workers in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: This report examines relationships between the acceptance of compensation claims, and employee and workplace characteristics for healthcare workers in British Columbia, Canada to determine suitability of using only accepted claims for occupational epidemiology research. METHODS: A retrospective cohort of full-time healthcare workers was constructed from an active incident surveillance database. Incidents filed for compensation over a 1-year period were examined for initial claim decision within a 6-month window relative to sub-sector of employment, age, sex, seniority, occupation of workers, and injury category. Compensation costs and duration of time lost for initially accepted claims were also investigated. Multiple logistic regression models with generalised estimating equations (GEEs) were used to calculate adjusted relative odds (ARO) of claims decision accounting for confounding factors and clustering effects. RESULTS: Employees of three health regions in British Columbia filed 2274 work-related claims in a year, of which 1863 (82%) were initially accepted for compensation. Proportion of claims accepted was lowest in community care (79%) and corporate office settings (79%) and highest in long-term care settings (86%). Overall, 46% of claims resulting from allergy/irritation were accepted, in contrast to 98% acceptance of claims from cuts and puncture wounds. Licensed practical nurses had the lowest odds of claims not accepted compared with registered nurses (ARO (95% CI) = 0.55 (0.33 to 0.91)), whereas management/administrative staff had the highest odds (ARO = 2.91 (1.25 to 6.79)) of claims not accepted. A trend was observed with higher seniority of workers associated with lower odds of non-acceptance of claims. CONCLUSIONS: Analysis from British Columbia's healthcare sector suggests variation in workers' compensation acceptance exists across sub-sectors, occupations, seniority of workers, and injury categories. The patterns observed, however, were independent of age and sex of workers. Results suggest that when using workers' compensation datasets, local adjudication regulations and factors associated with acceptance of claims should be taken into consideration.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".