Mental health effects of unemployment and re-employment: a systematic review and meta-analysis of longitudinal studies
Bibliographic record
Abstract
This systematic review examined the impact of unemployment and re-employment on mental health problems (depression, anxiety and psychological distress) among working-age adults. We searched MEDLINE, Embase, APA PsycINFO and Web of Science (January 2012-March 2024) and included studies from a prior meta-analysis (1990-2012). Risk of bias was assessed using the Newcastle-Ottawa Scale. We conducted random-effects meta-analyses and narrative synthesis and evaluated the certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Of 9328 search records, 38 prospective longitudinal studies met the inclusion criteria (27 from 2012-2024 and 11 from 1990-2012). A pooled standardised mean difference (SMD, Cohen's d) of 0.19 (95% CI 0.08 to 0.30, I²=88.7%) indicated increased symptom levels among the unemployed compared with those regularly employed. Similarly, pooled effect estimates indicated reduced symptoms after re-employment, with a stronger effect observed in between-group difference-in-difference analyses (SMD=-0.27, 95% CI -0.35 to -0.20, I²=40.1%) than within-group analyses (SMD=-0.19, 95% CI -0.29 to -0.10, I²=84.3%). The certainty of evidence for all outcomes based on our GRADE evaluation was low. Our systematic review and meta-analysis suggest that unemployment increases the risk of mental health problems, while re-employment may reduce this risk. However, due to the lack of high-certainty evidence, further longitudinal studies with multiple follow-ups are needed to strengthen causal inferences and better clarify mental health trajectories before and after re-employment.
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.
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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".