Mechanisms of vocational interventions for return to work from musculoskeletal conditions: a mediation analysis of the MI-NAV trial
Bibliographic record
Abstract
OBJECTIVES: To investigate whether and to what extent, return to work (RTW) expectancy and workability mediate the effect of two vocational interventions on reducing sickness absence in workers on sick leave from a musculoskeletal condition. METHODS: This is a preplanned mediation analysis of a three-arm parallel randomised controlled trial which included 514 employed working adults with musculoskeletal conditions on sick leave for at least 50% of their contracted work hours for ≥7 weeks. Participants were randomly allocated (1:1:1) to one of three treatment arms; usual case management (UC) (n=174), UC plus motivational interviewing (MI) (n=170) and UC plus a stratified vocational advice intervention (SVAI) (n=170). The primary outcome was the number of sickness absence days over 6 months from randomisation. Hypothesised mediators included RTW expectancy and workability assessed 12 weeks after randomisation. RESULTS: The mediated effect of the MI arm compared with UC on sickness absence days through RTW expectancy was -4.98 days (-8.89 to -1.04), and workability was -3.17 days (-8.55 to 2.32). The mediated effect of the SVAI arm compared with UC on sickness absence days through RTW expectancy was -4.39 days (-7.60 to -1.47), and workability was -3.21 days (-7.90 to 1.50). The mediated effects for workability were not statistically significant. CONCLUSIONS: Our study provides new evidence for the mechanisms of vocational interventions to reduce sickness absence related to sick leave due to musculoskeletal conditions. Changing an individual's expectation that RTW is likely may result in meaningful reductions in sickness absence days. TRIAL REGISTRATION NUMBER: NCT03871712.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".