Relationship between self-esteem and employment in people with severe mental illness: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Results from past research on the association between work outcomes and self-esteem were inconsistent. OBJECTIVE: This study aimed to review and quantify the correlation between employment variables and self-esteem in people with severe mental illness. METHOD: The first electronic database search was performed between November 5 and November 12, 2021. A second search update was completed in September 2023. Studies that reported a correlation between at least one employment-related variable and self-esteem were subsequently verified. Pooled effect sizes were calculated with random-effects models by aggregating Fisher’s Z-to-Pearson r transformed correlations. RESULTS: The database search generated 3,547 reports. Thirteen and seven reports were included in the qualitative review and the meta-analyses, respectively. Meta-analyses results based on data from 1,065 participants suggested a positive albeit small correlation between employment variables and self-esteem in people with severe mental illness (r = 0.26, p = .002 for global self-esteem; r = 0.21, p < 0.001 for total self-esteem). It was found through systematic review that greater confidence in personal capacity, more opportunities on novel activities, and positive affirmation from coworkers were some potential mechanisms underlying self-esteem improvement following work. CONCLUSION: Future research on employment in severe mental illness would benefit from including adapted self-esteem measures and can build on this work by examining the relationships between specific employment variables (e.g., job acquisition, job tenure) and self-esteem.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".