Underreporting Work Absences for Nontraumatic Work-Related Musculoskeletal Disorders to Workers’ Compensation: Results of a 2007–2008 Survey of the Québec Working Population
Bibliographic record
Abstract
OBJECTIVES: We examined underestimation of nontraumatic work-related musculoskeletal disorders (WMSDs) stemming from underreporting to workers' compensation (WC). METHODS: In data from the 2007 to 2008 Québec Survey on Working and Employment Conditions and Occupational Health and Safety we estimated, among nonmanagement salaried employees (NMSEs) (1) the prevalence of WMSDs and resulting work absence, (2) the proportion with WMSD-associated work absence who filed a WC claim, and (3) among those who did not file a claim, the proportion who received no replacement income. We modeled factors associated with not filing with multivariate logistic regression. RESULTS: Eighteen percent of NMSEs reported a WMSD, among whom 22.3% were absent from work. More than 80% of those absent did not file a WC claim, and 31.4% had no replacement income. Factors associated with not filing were higher personal income, higher seniority, shorter work absence, and not being unionized. CONCLUSIONS: The high level of WMSD underreporting highlights the limits of WC data for surveillance and prevention. Without WC benefits, injured workers may have reduced job protection and access to rehabilitation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".