Associations of Self-reported Musculoskeletal Pain and Depressive Symptoms among U.S. Healthcare Workers
Bibliographic record
Abstract
Healthcare workers are prone to develop musculoskeletal pain because of the physical demands of their profession. While neck and back pain are believed to have a relationship with depression symptomatology, few studies have assessed this relationship among healthcare workers. The purposes of this study were to identify the: prevalence of musculoskeletal pain and depressive symptoms among healthcare workers; association between musculoskeletal pain and depressive symptoms; and the association between musculoskeletal pain and severity of depressive symptomatology among those with self-reported depressive symptoms. Data from 1,205 healthcare workers in the 2018 National Health Insurance Survey were analyzed. In Phase 1, a logistic regression model was fitted to assess the relationship between self-reported neck and back pain and depressive symptoms. Then, in Phase 2, a logistic regression model was fitted for participants with self-reported depressive symptoms (n=501) to identify associations of neck and back pain with the severity of depressive symptomatology. About 74.9% of the study participants were female, 42.7% aged 41-64 years, 34.5% reported musculoskeletal pain, while 41.7% reported depressive symptoms. Low back pain was the most prevalent body pain (18.7%). Healthcare workers with neck pain only (OR=2.11, P=0.047), low back pain only (OR=2.19, PPHealthcare workers could benefit from multi-faceted public health interventions to simultaneously improve their musculoskeletal pain and depressive symptoms (e.g., ergonomic evaluation, stress management, one-on-one or group counseling).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".