Drivers of dropout and enhancers of engagement for male military veterans in therapy: practitioner perspectives
Bibliographic record
Abstract
Male veterans are vastly over-represented in suicide rates relative to non-veterans. A critical avenue for improving male veterans’ mental health outcomes is improving their engagement with mental health services. This study presents a qualitative investigation of mental health practitioners’ perspectives on enhancers of engagement in, and drivers of dropout from therapy among male veterans. Participants were 138 mental health practitioners across Australia, the USA, Canada, New Zealand and the UK (44.9% male; age M = 47.5 years, SD = 12 years). Participants responded to qualitative survey items inquiring about their perspectives on what works to engage male veterans in therapy, alongside common drivers of therapy dropout. Under an overarching theme contextualising the therapeutic alliance between veterans and mental health practitioners, interpretive description analyses led to eight distinct subthemes. Results highlight the range of areas in which mental health practitioners can thoughtfully adapt their practice to engage male veterans and align with military masculinities. In addition, findings underscore the range of barriers facing veterans when they seek help, which can precipitate dropout if not overcome by the right balance between practitioner engagement and veteran persistence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".