Examining associations between work-related injuries and all-cause healthcare use among middle-aged and older workers in Canada using CLSA data
Bibliographic record
Abstract
INTRODUCTION: Prior studies examining the relationship between work- related injuries and healthcare use among middle-aged and older workers were mainly cross-sectional and reported inconsistent results. OBJECTIVE: The objective of this study was to examine the associations between work-related injuries and 10 types of healthcare service use for any cause among middle-aged and older Canadian workers using longitudinal data. METHODS: Our study involved longitudinal analysis of baseline and 18-month follow-up Maintaining Contact Questionnaire data from the Canadian Longitudinal Survey on Aging (CLSA) for a national sample of Canadian males and females aged 45-85 years who worked or were recently retired (N = 24,748). RESULTS: Among CLSA participants who worked or were recently retired, 361 per 10,000 reported a work-related injury within the year prior to the survey. Work-related injuries decreased with increasing age. Work-related injury was associated with emergency department visits, overnight hospitalization, visits to dentists, and visits to physiotherapists, occupational therapists, or chiropractors at follow-up in bivariate analyses. Compared to those with no work-related injuries, Canadians with work-related injuries had used, on average, a significantly higher number of health services within the last 12 months prior their survey. When controlling for the contribution of various socio-demographic, work-related, and health-related characteristics, work-related injuries remained a significant predictor of emergency department visits and visits to physiotherapists, occupational therapists, or chiropractors. CONCLUSIONS: The relationship between work-related injuries, emergency department visits, and visits to physiotherapists, occupational therapists, or chiropractors in middle-aged and older workers in Canada suggests that workplace injuries can be associated with ongoing health problems. PRACTICAL APPLICATIONS: Healthcare services used by injured employees must be considered priorities for employment insurance coverage, if not already covered. Future research should more fully examine whether pre-existing health conditions predict both work-related injury and subsequent health problems. Injury-specific healthcare use following work-related injuries in middle-aged and older workers, as well as economic costs, should also be examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.006 |
| 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".