Family members of Veterans with mental health problems: Seeking, finding, and accessing informal and formal supports during the military-to-civilian transition
Bibliographic record
Abstract
LAY SUMMARY Veterans and their families in the military-to-civilian transition (MCT) face a multitude of changes and challenges. Family members of those Veterans experiencing a significant mental health problem (e.g., posttraumatic stress disorder, depression, anxiety) may find that navigating the MCT is made more complex, especially as they seek to find social support during this transition. The present study set out to hear from family members and learn about their obstacles and successes in accessing formal and informal support during the MCT and how this was affected by the Veteran’s mental health problems. Interviews and focus groups were completed with 36 English- and French-speaking Veteran family members across Canada. Family members shared how significant issues (e.g., mental health stigma, caregiver burden and burnout) were barriers to seeking and finding both informal (i.e., extended family, friends, online support) and formal (i.e., operational stress injury clinics, Military Family Resource Centres) support systems helpful in navigating the MCT. Despite setbacks and frustrations in accessing these supports, Veteran military families demonstrated resiliency and resolve as they pursued comfort, financial aid, respite, and counsel for themselves and for the Veteran with mental health problems during the MCT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".