The prognostic value of depressive symptoms, fear-avoidance, and self-efficacy for duration of lost-time benefits in workers with musculoskeletal disorders
Bibliographic record
Abstract
BACKGROUND: The psychological factors of depressive symptoms, fear-avoidance, and self-efficacy are deemed to be important in the work disability process. However, the prognostic value of these factors for time on benefit is not well understood. AIMS: To analyse the prognostic value of psychological factors for the number of days on total compensation benefit over a 12 month period. METHODS: In a longitudinal study of 187 workers receiving total compensation benefits due to musculoskeletal disorders, the prognostic value of psychological factors measured 4-5 weeks post-injury for duration on total compensation benefit over 12 months was analysed. Cox proportional hazard regression analyses were conducted. Special emphasis was given to variable selection and to the analysis of confounding effects of potential prognostic variables. RESULTS: The final model indicated that increased depressive symptoms and poorer physical health significantly increase the number of days on total benefit. Confounders included in the final model were pain and fear of income loss. In the final model the impact of fear-avoidance ceased to be significant when work related variables were included in the fully adjusted model. This illustrates that interrelationships between variables must be taken into account when building multivariate prognostic models. The addition of work related variables to the model did not result in any major changes in the adjusted model, which suggests that when measured 4-5 weeks post-injury, psychological and physical health factors are strong predictors of time on benefits, while work conditions are less important. CONCLUSION: Results suggest that the presence of depressive symptoms and poor physical health in workers on benefit due to musculoskeletal disorders increases the number of days on total compensation benefits significantly, when controlling for confounding variables.
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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 | 0.000 | 0.000 |
| 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".