Uncovering Mental Health Profiles of Workers with a Physically Disabling Injury or Illness Using the Complete State Mental Health Framework
Bibliographic record
Abstract
BACKGROUND: Complete mental health encompasses both mental illness (MI) symptoms and positive mental health (PMH). Distinct profiles of MI and PMH have not been explored among injured workers. This study describes latent mental health profiles among workers with a disabling physical work injury/illness and identifies differences in sociodemographic and return-to-work factors, health correlates, and disability claim duration and cost between profiles. METHODS: 1132 Ontario workers with a physical work-related injury/illness who received lost-time claim benefits were surveyed 18 months post-injury. MI was defined by the self-reported presence of a mood and/or anxiety disorder diagnosed by a healthcare professional pre- or post-injury. The Mental Health Continuum Short Form measured aspects of PMH. Claim information was obtained via administrative records. Latent profile analysis identified the unique number of MI and PMH profiles. Chi-Square and ANOVA tests compared sociodemographic, return-to-work, health, and claim outcomes between classes. RESULTS: Four latent MI and three latent PMH classes were uncovered. Eighteen percent of participants exhibited high MI symptoms diagnosed pre- and post-injury and 14% exhibited languishing PMH. Classes with higher MI burden and languishing PMH were more likely to report financial concerns during their claim, pain interference, other health conditions, and opioid use. Claim duration and wage-replacement benefits were ~ 20 days longer and ~ $2000 greater, respectively, among the highest MI and lowest PMH classes. CONCLUSIONS: Workers' compensation claimants exhibit both flourishing and languishing mental health profiles. The demographic, health, and return-to-work characteristics of latent classes may help identify claimants who may benefit from additional psychological support when 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".