Course, Diagnosis, and Treatment of Depressive Symptomatology in Workers following a Workplace Injury: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To estimate prevalence, incidence, and course of depressive symptoms and prevalence of mental health treatment following a workplace injury, and to estimate the association between depressive symptoms and return-to-work (RTW) trajectories. METHOD: In a prospective cohort study, workers filing a lost-time compensation claim for a work-related musculoskeletal disorder of the back or upper extremity were interviewed 1 month (n = 599) and 6 months (n = 430) postinjury. A high level of depressive symptoms was defined as 16 or more on the self-reported Center for Epidemiologic Studies-Depression (CES-D) Scale. The following estimates are reported: prevalence of high depressive symptom levels at 1 and 6 months postinjury; incidence, resolution, and persistence of high depressive symptom levels between 1 and 6 months; and prevalence of self-reported mental health treatment and depression diagnosis at 6 months postinjury. RESULTS: Prevalence of high depressive symptom levels at 1 month and 6 months postinjury were 42.9% (95% CI 38.9% to 46.9%) and 26.5% (95% CI 22.3% to 30.7%), respectively. Among participants reporting high depressive symptom levels at 1 month postinjury, 47.2% (95% CI 39.9% to 54.5%) experienced a persistence of symptoms 6 months postinjury. By 6 months, 38.6% of workers who never returned to work or had work disability recurrences had high depressive symptom levels, compared with 17.7% of those with a sustained RTW trajectory. At 6-month follow-up, 12.9% (95% CI 5.8% to 20.1%) of participants with persistently high depressive symptom levels self-reported a depression diagnosis since injury and 23.8% (95% CI 14.7% to 32.9%) were receiving depression treatment. CONCLUSIONS: Depressive symptoms are pervasive in workers with musculoskeletal injuries, but transient for some, and seldom diagnosed as depression or treated.
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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 | 0.001 |
| 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".