The Prevalence and Incidence of Work Absenteeism Involving Neck Pain
Bibliographic record
Abstract
In Brief Study Design. Cohort study. Objective. To measure the prevalence and incidence of work absenteeism involving neck pain in a cohort of claimants to the Ontario Workplace Safety & Insurance Board (WSIB). Summary of Background Data. According to workers’ compensation statistics, neck pain accounts for a small proportion of lost-time claims. However, these statistics may be biased by an underenumeration of claimants with neck disorders. Methods. We studied all lost-time claimants to the Ontario WSIB in 1998 and used 2 methods to enumerate neck pain cases. We report the prevalence and incidence of neck pain using 2 denominators: (1) annual number of lost-time claimants and (2) an estimate of the Ontario working population covered by the WSIB. Results. The estimated percentage of lost-time claimants with neck pain ranged from 2.8% (95% CI 2.5–3.3) using only codes specific for neck pain to 11.3% (95% CI 9.5–13.1) using a weighted estimate of codes capturing neck pain cases. The health care sector had the highest percentage of claims with neck pain. The annual incidence of neck pain among the Ontario working population ranged from 6 per 10,000 full-time equivalents (FTE) (95% CI 5–6) to 23 per 10,000 FTE (95% CI 20–27) depending on the codes used to capture neck pain. Male workers between the ages of 20 and 39 years were the most likely to experience an episode of work absenteeism involving neck pain. Conclusion. Neck pain is a common and burdensome problem for Ontario workers. Our study highlights the importance of properly capturing all neck pain cases when describing its prevalence and incidence. We studied the prevalence and incidence of lost-time claims involving neck pain in Ontario. In 1998, 11.3% of injured workers with lost-time claims had neck pain. The annual incidence in the Ontario working population was 23 per 10,000 FTE. Neck pain is a common source of disability in Ontario workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".