Interventions for enhancing return to work in individuals with a common mental illness: systematic review and meta-analysis of randomized controlled trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Common mental disorders (CMDs) are highly prevalent in the working population, and are associated with long-term sickness absence and disability. Workers on sick leave with CMDs would benefit from interventions that enable them to successfully return to work (RTW). However, the effectiveness of RTW interventions for workers with a CMD is not well studied. The objective of this review is to assess the effectiveness of existing workplace and clinical interventions that were aimed at enhancing RTW. A systematic review of studies of interventions for improving RTW in workers with a CMD was conducted. The main outcomes were proportion of RTW and sick-leave duration until RTW. Randomized controlled trials (RCTs) were identified from Medline/PubMed, PsycINFO, EMBASE, SocINDEX, and Human resource and management databases from January 1995 to 2016. Two authors independently selected studies, assessed risk of bias and extracted data. We pooled studies that we deemed sufficiently homogeneous in different comparison groups and assessed the overall quality of the evidence. We reviewed 2347 abstracts from which 136 full-text articles were reviewed and 16 RCTs were included in the analysis. Combined results from these studies suggested that the available interventions did not lead to improved RTW rates over the control group [pooled risk ratio 1.05, 95% confidence interval (CI) 0.97-1.12], but reduced the number of sick-leave days in the intervention group compared to the control group, with a mean difference of -13.38 days (95% CI -24.07 to -2.69).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.082 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.084 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it