Psychological health and safety in the Canadian Defence Team
Bibliographic record
Abstract
Introduction: aims to ensure its Defence Team (DT) personnel's needs are met. Various Department of National Defence (DND) and Canadian Armed Forces (CAF) strategies focus on enhancing personnel well-being and organizational culture. This study assessed perceptions and experiences related to psychological health, organizational culture, and retention among DT personnel. The goal was to explore relationships between workplace factors and outcomes among Regular Force (Reg F) CAF members, Primary Reserve (P Res) CAF members, and DND public service (PS) personnel, and to determine whether associations between certain workplace factors and outcomes of interest differed across the three groups. Methods: An online survey was administered to stratified random samples from each component in the spring or summer of 2022. The final sample included 4,463 Reg F members, 1,318 P Res members, and 2,866 DND PS personnel. Results: Despite moderate morale, burnout, psychological distress, and affective commitment to the CAF/DND, DT personnel are generally satisfied, engaged, and not planning to leave within the next 12 months. Several workplace factors, particularly pride in organizational membership and perceptions of organizational support, were associated with outcomes of interest among all groups. Meaningful work was a key driver only among CAF members. The strength of associations between workplace factors and outcomes of interest varied across components. Discussion: These results aim to help CAF/DND leadership foster DT personnel's well-being and improve the organizational culture, with tailored recommendations that address the specific needs of each component.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".