Families' experiences of supporting Australian veterans and emergency service first responders ( <scp>ESFRs</scp> ) to seek help for mental health problems
Bibliographic record
Abstract
The objective of this phenomenological study was to describe families' experiences of supporting veterans and emergency service first responders (ESFRs) (known also as public safety personnel) to seek help for a mental health problem. In-depth semi-structured open-ended interviews were undertaken with 25 family members of Australian veterans and ESFRs. Fourteen participants were family members of police officers. Data were analysed thematically. Participants described a long and difficult journey of supporting the person's help-seeking across six themes. Traumatic exposures, bullying in the workplace and lack of organisational support experienced by veterans/ESFRs caused significant family distress. Families played a vital role in help-seeking but were largely ignored by veteran/ESFR organisations. The research provides a rich understanding of distress and moral injury that is experienced not only by the service members but is transferred vicariously to their family within the mental health help-seeking journey. Veteran and ESFR organisations and mental health services need to shift from a predominant view of distress as located within an individual (intrapsychic) towards a life-course view of distress as impacting families and which is more relational, systemic, cultural and contextual.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".