Families’ experiences of supporting Australian veterans to seek help for a mental health problem: a linked data analysis of national surveys with families and veterans
Bibliographic record
Abstract
BACKGROUND: Families play a critical role in supporting currently serving and transitioned veterans' wellbeing and help-seeking for mental health concerns; however, little is known about families' experiences. AIMS: This study used Australian national survey linked-data (n = 1217) from families (Family Wellbeing Study-FWS) and veterans (Mental Health Wellbeing Transition Study-MHWTS) to understand veteran-family help-seeking relationships. METHODS: Veterans' and family members' responses to mental health and help-seeking questions in FWS and MHWTS datasets from perspective of family members were cross-tabulated. Help-seeking support provided by family members was compared by veterans' probable disorder. RESULTS: Results highlighted high levels of involvement and continuous assistance provided by families. Two in three family members thought the veteran had probable mental health concerns although they have never been diagnosed or treated. Clear disparities between family and veteran perspectives regarding mental health concerns indicates the extent of non-treatment seeking in this population, missed opportunities for early intervention, and need for greater support to families to promote help-seeking. CONCLUSIONS: Encouraging help-seeking is complex for veteran families particularly where veterans' reluctance to seek help may lead to family relationship strain and conflict. Families need early information, support, and recognition by service agencies of the role of the family in encouraging help-seeking.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".