Sleep disturbances and disability following work‐related injury and illness: Examining longitudinal relationships across three follow‐up waves
Bibliographic record
Abstract
Despite the high burden of sleep disturbances among the general population, there is limited information on prevalence and impact of poor sleep among injured workers. This study: (a) estimated the prevalence of sleep disturbance following work-related injury; and (b) examined the longitudinal association between sleep disturbances and disability/functioning, accounting for reciprocal relationships and mental illness. Longitudinal survey data were collected from workers' compensation claimants with a time-loss claim in Victoria, Australia (N = 700). Surveys were conducted at baseline, 6 months and 12 months. Sleep disturbance was measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire. Disability/functioning was based on self-reported activity limitations, participation restrictions and emotional functioning. Path models examined the association between disability/functioning and sleep. Mean sleep disturbance T-scores were 55.2 (SD 11.4) at 6 months, with 36.4% of the sample having a T-score of 60+. Longitudinal relationships were observed between disability (specifically, emotional functioning) and sleep disturbances across successive follow-up waves. For example, each unit increase in T2 emotional functioning (five-point scale) was associated with a 1.1 unit increase in T3 sleep disturbance (approximately 29-76 scale). Cross-lagged path models found evidence of a reciprocal relationship between disability and sleep, although adjustment for mental illness attenuated the estimates to the null. In conclusion, sleep disturbances are common among workers' compensation claimants with work injuries/illnesses. Given the links between some dimensions of disability, mental health and sleep disturbances, the findings have implications for the development of interventions that target the high prevalence of sleep problems among working populations.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".