Involving families in Veteran mental health care: Key considerations and recommendations
Bibliographic record
Abstract
LAY SUMMARY When Veterans seek and receive mental health care, their family members are often involved, directly or indirectly, in the process. Within Canada, recognition of the need for family-centred policies and practices is growing; however, family involvement in care is generally the exception rather than the rule. A recently developed Conceptual Framework advocates for a transformed mental health system centred on the experiences, needs, and preferences of Veterans and their families. This system may be well suited to a shift toward family-involved care. Drawing on this Framework, this article makes a case for specifically involving families in Veteran mental health care. In particular, the crucial relationship between family and Veteran well-being is considered, as well as key benefits of and potential barriers to involvement. With these considerations in mind, some recommendations are made to move research, practice, and policy forward. These include 1) formalizing the definition of family, 2) developing a more comprehensive and nuanced understanding of Veteran families and their involvement, and 3) using educational and guidance materials to improve knowledge and build capacity. Despite potential limitations, these considerations and recommendations offer an opportunity to advance dialogue related to meaningful and safe involvement of families in Veteran mental health care.
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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 | 0.041 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".