Non-pharmacological interventions for mental health among partners of military members and Veterans: A systematic review
Bibliographic record
Abstract
Introduction: Partners of military members and Veterans face the common challenges that typical families encounter while also dealing with unique difficulties arising from their partners' risky deployments and frequent relocations. This study's objective was to summarize the contents and effectiveness of non-pharmacological interventions in enhancing mental health among partners of military members and Veterans. Methods: The authors searched four electronic databases - PubMed, PsycINFO, EMBASE, and the Cochrane Register of Clinical Trials - covering studies published between 2012 and 2022. Results: This study incorporated 13 articles, comprising six randomized controlled trials (RCT) and seven non-RCT studies. Psycho-education demonstrated significant positive effects on depression, anxiety, stress, and problem-solving/communication. Cognitive behavioural therapy was employed in two studies, with only one indicating a noteworthy reduction in stress and anxiety levels. Mind-body integrated programs were employed in two studies, both revealing significant effects on depression, anxiety, stress, and PTSD symptoms. Lastly, one study implemented a social support intervention, yielding positive enhancements in family relationships. Discussion: Overall, non-pharmacological interventions exhibited positive improvements in the mental health of military partners. In particular, psychoeducation, encompassing a sufficient number of sessions with skills training, demonstrated benefits in alleviating symptoms of depression and anxiety. Future research endeavors require a sufficient number of studies with standardized formats and high methodological quality, enabling meta-analyses to quantify the impact of non-pharmacological interventions.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".