Role of modifiable organisational factors in decreasing barriers to mental healthcare: a longitudinal study of mission meaningfulness, team relatedness and leadership trust among Canadian military personnel deployed on Operation LASER
Bibliographic record
Abstract
OBJECTIVES: The literature presents complex inter-relationships among individual-factors and organisational-factors and barriers to seeking mental health support after deployment. This study aims to quantify longitudinal associations between such factors and barriers to mental health support. DESIGN: A longitudinal online survey of Canadian Armed Forces (CAF) personnel collected data at 3 months post-deployment (T1), 6 months post-deployment (T2) and 1 year post-deployment (T3). SETTING: In 2020, as part of Canada's response to the COVID-19 pandemic, 2595 CAF personnel deployed on Operation LASER to support civilian long-term care facilities in Québec and Ontario. PARTICIPANTS: All Operation LASER personnel were invited to participate: 1088, 582 and 497 responded at T1, T2 and T3, respectively. Most respondents were young, male, non-commissioned members. MAIN OUTCOME MEASURES: Barriers to mental health support were measured using 25 self-reported items and grouped into theory-based factors, including eight factors exploring care-seeking capabilities, opportunities and motivations; and two factors exploring moral issues. Logistic regressions estimated the crude and adjusted associations of individual and organisational characteristics (T1) with barriers (T2 and T3). RESULTS: When adjusting for sex, military rank and mental health status, increased meaningfulness of deployment was associated with lower probability of endorsing barriers related to conflicts with career goals and moral discomfort in accessing support at T2. Higher scores in trust in leadership were associated with lower probability of endorsing four barriers at T2, and five barriers at T3. CONCLUSIONS: We identified several modifiable organisational-level characteristics that may help reduce perceived barriers to mental health support in military and other high-risk occupational populations. Results suggest that promoting individuals' sense of purpose, instilling trust in leadership and promoting relatedness among team members may improve perceptions of access to mental health supports in the months following a domestic deployment or comparable occupational exposure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".