Families’ moral distress when supporting military Veteran and public safety personnel’s mental health: Conceptual model
Bibliographic record
Abstract
Introduction: Families are vital in supporting the mental health and well-being of military Veterans and public safety personnel (PSP; e.g., police, ambulance, fire, and emergency services), yet they can feel that services exclude them. The objective of this study was to describe families' experiences of supporting Veterans/PSP seeking help for mental health concerns and formulate a conceptual model to illustrate the impacts of these experiences on families. Methods: The conceptual model was informed by thematic analyses of in-depth semi-structured interviews conducted in Australia with 25 family members of Veterans/PSP. Results: Families were deeply embedded and aligned to their family member's role in the community, with significant empathy for sense of duty, and a profound sense of betrayal and distress when attempts to support family members were perceived as blocked or challenged. The conceptual model demonstrates families' help-seeking processes and how they may vicariously experience moral distress from being caught in a liminal space in which they can see the problem and potential support solutions but have no options to realize timely supports for family members. Discussion: This study offers a detailed model of how moral distress can arise for families of Veterans/PSP who experience mental health concerns. It demonstrates how organizational culture at Departments of Defence, Veterans' Affairs, and public safety groups exclude families, exacerbating a sense of moral distress. Implications and recommendations for Veteran/PSP organizations and health professionals to promote more meaningful involvement and consideration of families is discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".