The impact of employment programs on common mental disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: While employment programs were not created with the intent to improve common mental disorders (CMDs), they may have a positive impact on the prevalence, incidence, and severity of CMD by reducing poverty and increasing access to economic mobility. AIM: To examine and synthesize the available quantitative evidence of the impact of employment programs on outcomes of CMD. METHODS: Embase, Econlit, Global Health, MEDLINE, APA PsychINFO, and Social Policy and Practice were searched for experimental and quasi-experimental studies which investigated the impact of employment programs on primary and secondary outcomes of a CMD. A narrative synthesis according to Popay was conducted. The methodological quality of studies was assessed with the Cochrane Risk of Bias tool and the Newcastle-Ottawa Assessment Scale. RESULTS: Of the 1,327 studies retrieved, two randomized controlled trials, one retrospective cohort, one pilot study with a non-randomized experimental design, and one randomized field experiment were included in the final review. Employment programs generally included multiple components such as skills-based training, and hands-on placements. Depression and anxiety were the CMDs measured as primary or secondary outcomes within included studies. Findings regarding the impact of employment programs on CMD were mixed with two studies reporting significantly positive effects, two reporting no effects, and one reporting mixed effects. The quality among included studies was good overall with some concerns regarding internal validity. CONCLUSION: Employment programs may support a decrease in the prevalence, incidence, and severity of CMDs. However, there is high heterogeneity among study effects, designs, and contexts. More research is needed to gain further insight into the nature of this association and the mechanisms of impact. This review highlights the potential for employment programs and other poverty-reduction interventions to be utilized and integrated into the wider care, prevention, and treatment of common-mental 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".