Optimizing military mental health and stress resilience training through the lens of trainee preferences: A conjoint analysis approach
Bibliographic record
Abstract
Effective mental health and stress resilience (MHSR) training is essential in military populations given their exposure to operational stressors. The scarcity of empirical evidence supporting the benefits of these programs emphasizes the need for research dedicated to program optimization. This paper aims to identify the relative importance of MHSR training attributes preferred by military members. Conjoint analysis (CA), an experimental method used to prioritize end-user preferences for product feature development, was conducted using an online survey with 567 Canadian Armed Forces (CAF) personnel. Participants made a series of choices between hypothetical MHSR training options that were systematically varied across seven training attributes. Each training attribute consisted of 3-4 variations in the nature of the attribute or its intensity. Participants also completed questions on health beliefs, mental health and previous MHSR training experiences, and demographics, to assess whether preferences varied by individual characteristics. CA demonstrated that instructor type, leadership buy-in, degree of skills practice, and content relevance/applicability were attributes of highest and relatively equal importance. This was followed by degree of accessible supplemental content. Lowest importance was placed on degree of behavioral nudging and demographic similarity between the trainee and trainer. Sociodemographic factors were not associated with MHSR training preferences. Programs that incorporate expert-led instruction, demonstrate leadership buy-in, embed practical applications within simulated stress environments, and provide a digitally-accessible platform to augment training may be well-received among military members. Understanding and accommodating personal preferences when designing MHSR training programs may increase relevance, foster acceptance and trust, and support sustained engagement.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".