Delivering services to the families of Veterans of current conflicts: a rapid review of outcomes for Veterans
Bibliographic record
Abstract
Introduction: Military service affects the health and well-being of both Veterans and their family members, with increasing recognition of the need for support to be provided to Veterans’ families. In 2017 the Australian Department of Veterans’ Affairs commissioned a rapid review of the literature to explore whether the delivery of support services to families of contemporary Veterans results in better outcomes for the Veteran. Methods: A systematic search of the peer-reviewed literature resulted in the extraction of 30 articles, reduced to 29 following quality assessment. We then produced a narrative synthesis of the articles. Results: All of the studies were undertaken in the United States (US). The majority ( n = 20) focused on couples/family therapy, in particular, to address post-traumatic stress disorder (PTSD) in Veterans. Discussion: The studies overall reported significant psychological and relational benefit for Veterans when families are supported. However, the diversity of approaches suggests that currently, no one approach suits all family situations. More work is needed to develop interventions that address the broader psychosocial needs of families, such as work commitments and childcare needs, and to evaluate how these might benefit Veterans.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".