Association between modifiable social determinants and mental health among post-9/11 Veterans: A systematic review
Bibliographic record
Abstract
Introduction: As U.S. Veterans reintegrate from active duty to civilian life, many are at risk for negative modifiable social determinants of health. The prevalence of mental health conditions among Veterans is also high. Awareness of the associations between these two factors is growing. This systematic review provides a comprehensive analysis of the current state of knowledge of the associations between modifiable social determinants and mental health among U.S. Veterans. Methods: The authors systematically searched four databases and identified 28 articles representing 25 unique studies that met inclusion criteria. Findings from the studies were extracted and synthesized on the basis of modifiable social determinants. Study quality and risk of bias were assessed using the Methodological Quality Questionnaire. Results: The studies identified in the systematic review examined three modifiable social determinants of health: 1) housing stability, 2) employment and finances, and 3) social support. Although the lack of validity for measures of housing stability, employment, and finances compromised study quality, the overall evidence suggests that Veterans with access to supportive social determinants had better mental health status. Evidence was particularly robust for the association between strong social support and lower symptoms of posttraumatic stress disorder. Discussion: Current evidence suggests the need to consider modifiable social determinants of health when designing mental health interventions. However, more research encompassing a wider range of modifiable social determinants such as food security, education, and transportation and using comprehensive methods and validated instruments is needed. Future research also needs to intentionally include Veterans from diverse racial-ethnic groups.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".