Healthcare use before and after a workplace injury in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: There is growing evidence that occupational injuries influence workers' emotional and physical wellbeing, extending healthcare use beyond what is covered by the Workers' Compensation Board (WCB). METHODS: The authors used an administrative database that links individual publicly funded healthcare and WCB data for the population of British Columbia (BC), Canada. They examined change in service use, relative to one year before the injury, for workers who required time off for their injuries (lost time = LT) and compared them to other injured workers (no lost time = NLT) and individuals in the population who were not injured (non-injured = NI). RESULTS: LT workers increased physician visits (22%), hospital days (50%), and mental healthcare use (43% physician visits; and 70% hospital days) five years after the injury, relative to the year before the injury, at a higher rate than the NI group. For the NLT workers, the level of increased use following the injury was between that of these two groups. These patterns persisted when adjusting for registration in the BC Medical Service Plan (MSP) and several workplace characteristics. CONCLUSIONS: Although the WCB system is the primary mechanism for processing claims and providing information about workplace injury, it is clear that the consequences of workplace injury extend beyond what is covered by the WCB into the publicly funded healthcare system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".