Association Between Pain Coping and Symptoms of Anxiety and Depression, and Work Absenteeism in People With Upper Limb Musculoskeletal Disorders: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the prospective association of pain coping strategies and symptoms of anxiety and depression with work absenteeism in people with upper limb musculoskeletal disorders. DATA SOURCES: A systematic search of PubMed, Web of Science, Embase, Cochrane Library, and Scopus databases was conducted from inception to September 23, 2022. STUDY SELECTION: Prospective observational studies of adults with upper limb musculoskeletal disorders were included. Included studies had to provide data on the association of pain coping strategies (catastrophizing, kinesiophobia, self-efficacy or fear avoidance) or symptoms of anxiety and depression with work absenteeism. DATA EXTRACTION: Study selection, data extraction, and assessment of methodological quality (Newcastle Ottawa Scale) were performed by 2 independent authors. Random-effects models were used for quantitative synthesis. DATA SYNTHESIS: Eighteen studies (n=12,393 participants) were included. Most studies (77.8%) reported at least 1 significant association between 1 or more exposure factors (pain coping strategies or symptoms of anxiety and depression) and work absenteeism. Meta-analyses showed a statistically significant correlation between the exposure factors of catastrophizing (r=0.28, 95% confidence interval [CI]: 0.15 to 0.40; P<.0001) and symptoms of anxiety and depression (r=0.23, 95% CI: 0.10 to 0.34; P=.0003) with work absenteeism. The correlation between self-efficacy and work absenteeism was non-significant (r=0.24, 95% CI: -0.02 to 0.47; P=.0747). CONCLUSIONS: Rehabilitation teams should consider assessing catastrophizing and symptoms of anxiety and depression to identify patients at risk for work absenteeism. Addressing these variables may also be considered in return-to-work programs for individuals with upper limb disorders.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".