Time trends in musculoskeletal disorders attributed to work exposures in Ontario using three independent data sources, 2004–2011
Bibliographic record
Abstract
OBJECTIVE: Work-related musculoskeletal disorders (MSDs) are the leading cause of work disability in the developed economies. The objective of this study was to describe trends in the incidence of MSDs attributed to work exposures in Ontario over the period 2004-2011. METHODS: An observational study of work-related morbidity obtained from three independent sources for a complete population of approximately six million occupationally active adults aged 15-64 in the largest Canadian province. We implemented a conceptually concordant case definition for work-related non-traumatic MSDs in three population-based data sources: emergency department encounter records, lost-time workers' compensation claims and representative samples of Ontario workers participating in consecutive waves of a national health interview survey. RESULTS: Over the 8-year observation period, the annual per cent change (APC) in the incidence of work-related MSDs was -3.4% (95% CI -4.9% to -1.9%) in emergency departments' administrative records, -7.2% (-8.5% to -5.8%) in lost-time workers' compensation claims and -5.3% (-7.2% to -3.5%) among participants in the national health interview survey. Corresponding APC measures for all other work-related conditions were -5.4% (-6.6% to -4.2%), -6.0% (-6.7% to -5.3%) and -5.3% (-7.8% to -2.8%), respectively. Incidence rate declines were substantial in the economic recession following the 2008 global financial crisis. CONCLUSIONS: The three independent population-based data sources used in this study documented an important reduction in the incidence of work-related morbidity attributed to non-traumatic MSDs. The results of this study are consistent with an interpretation that the burden of non-traumatic MSDs arising from work exposures is declining among working-age adults.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".