Prevalence of serious mental illness and mental health service use after a workplace injury: a longitudinal study of workers’ compensation claimants in Victoria, Australia
Bibliographic record
Abstract
OBJECTIVES: Serious mental illness is common among those who have experienced a physical workplace injury, yet little is known about mental health service use in this population. This study aims to estimate the proportion of the workplace musculoskeletal injury population experiencing a mental illness, the proportion who access mental health services through the workers' compensation system and the factors associated with likelihood of accessing services. METHODS: A longitudinal cohort study was conducted with a random sample of 615 workers' compensation claimants followed over three survey waves between June 2014 and July 2015. The primary outcome was receiving any type of mental health service use during this period, as determined by linking survey responses to administrative compensation system records for the 18 months after initial interview. RESULTS: Of 181 (29.4%) participants who met the case definition for a serious mental illness at one or more of the three interviews, 75 (41.4%) accessed a mental health service during the 18-month observation period. Older age (OR=0.96, 95% CI 0.93 to 0.99) and achieving sustained return to work (OR=0.27, 95% CI 0.11 to 0.69) were associated with reduced odds of mental health service use. Although not significant, being born in Australia was associated with an increased odds of service use (OR=2.23, 95% CI 0.97 to 5.10). CONCLUSIONS: The proportion of injured workers with musculoskeletal conditions experiencing mental illness is high, yet the proportion receiving mental health services is low. More work is needed to explore factors associated with mental health service use in this population, including the effect of returning to work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".