Trends in the Health Care Use and Expenditures Associated With No-Lost-Time Claims in Ontario
Bibliographic record
Abstract
OBJECTIVE: To examine trends in health care usage and expenditures associated with no-lost-time claims in Ontario over a 15-year period. METHODS: A secondary analysis of administrative workers' compensation claims occurring between 1991 and 2006 (N = 2,290,101). We used regression analysis to model health care expenditures using a zero-inflated linear model, adjusting for age, gender, industry group and size of payroll. RESULTS: The probability of using health care increased over the time period. Health care expenditures per claim declined between 1991 and 1997, but then increased between 1998 and 2006, coinciding with the introduction of occupational health and safety legislation promoting early return to work in Ontario. CONCLUSIONS: Our results provide support to the hypothesis that the increasing use of workplace accommodation since 1998 is a driver of the relatively stable rate of no-lost-time claims in Ontario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".