Understanding the emotional and practical support needs of family care-partners of Veterans to promote well-being
Bibliographic record
Abstract
Introduction: There is limited research that explores the physical and psychological well-being of family care-partners of Australian Veterans. This article expands the understanding of the well-being of this group and identifies the emotional and practical supports required, whether supports were accessible, and the impact of caring on care-partner well-being. Methods: This mixed methods, cross-sectional research assessed physical and psychological health, health behaviours, resilience, and caregiving factors, using objective measures including the Kessler-10, International Physical Activity Questionnaire (short-form), 12-Item Short Form Survey, Brief Resilience Scale, Mental Health Continuum, Coping Orientation to Problems Experienced Inventory, and Caregiver Reaction Scale. Semi-structured interviews helped obtain deeper insights into the lived experiences of family care-partners. Results: A total of 103 surveys and 28 interviews were completed by family care-partners (97.1% female). The findings reflect a population with a high level of psychological distress, in the low end of normal resilience levels, and moderate mental health. The need for culturally safe services that accommodate working care-partners was identified, as was having support from people who understand the care-partner's situation. Care-partners reported varied impacts of their caring roles on the ability to engage in day-to-day life. Discussion: Although cross-sectional, the evidence suggests that the psychological well-being of Veteran care-partners is impacted by their caring role, with far-reaching implications for care-partners and the Veteran family unit. Commitments from government and defence organizations are required to support Veteran family units from recruitment, through service, deployment, transition to civilian life, in retirement, and old age. Co-designed approaches are required to ensure access to the emotional and practical supports identified by care-partners.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".