Healthcare Use of Families of Injured Workers Before and After a Workplace Injury in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: To examine the overall healthcare and mental healthcare services use of families of injured workers before and after a workplace injury. METHODS: We use an administrative database that links individual publicly funded healthcare data and Workers' Compensation Board (WCB) data for the entire population of British Columbia (BC), Canada. The spouses and children of all injured workers who filed a WCB claim in 1994 and missed one or more days of work due to the injury (lost time) were included. We compare their change in use of healthcare services relative to a year before the injury to families of workers who did not require time off for their injuries (no lost time) and families of individuals who were not injured (non-injured comparisons). RESULTS: Differences in healthcare services use among the three groups of spouses were marginal, and differences for increases in mental healthcare services use were non-significant. As well, all three groups of children decreased their use of physician and hospital services and increased their use of mental healthcare services, with very little difference among groups. CONCLUSION: This was a descriptive study looking at a broad group of injured workers and their families. Even modest increases in healthcare use following a workplace injury have some basis for further study.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".