Work Reintegration for Veterans With Mental Disorders: A Systematic Literature Review to Inform Research
Bibliographic record
Abstract
BACKGROUND: Some veterans, and especially those with mental disorders, have difficulty reintegrating into the civilian workforce. PURPOSE: The objectives of this study were to describe the scope of the existing literature on mental disorders and unemployment and to identify factors potentially associated with reintegration of workers with mental disorders into the workforce. DATA SOURCES: The following databases were searched from their respective inception dates: MEDLINE, EMBASE, Cumulative Index Nursing Allied Health (CINAHL), and PsycINFO. STUDY SELECTION: In-scope studies had quantitative measures of employment and study populations with well-described mental disorders (eg, anxiety, depression, posttraumatic stress disorder, substance-use disorders). DATA EXTRACTION: A systematic and comprehensive search of the relevant published literature up to July 2009 was conducted that identified a total of 5,195 articles. From that list, 81 in-scope studies were identified. An update to July 2012 identified 1,267 new articles, resulting in an additional 16 in-scope articles. DATA SYNTHESIS: Three major categories emerged from the in-scope articles: return to work, supported employment, and reintegration. The literature on return to work and supported employment is well summarized by existing reviews. The reintegration literature included 32 in-scope articles; only 10 of these were conducted in populations of veterans. LIMITATIONS: Studies of reintegration to work were not similar enough to synthesize, and it was inappropriate to pool results for this category of literature. CONCLUSIONS: Comprehensive literature review found limited knowledge about how to integrate people with mental disorders into a new workplace after a prolonged absence (>1 year). Even more limited knowledge was found for veterans. The results informed the next steps for our research team to enhance successful reintegration of veterans with mental disorders into the civilian workplace.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".