Investigating How Job Demands and Resources Affect Military Members’ Psychological Well-being with Military Survey Data
Bibliographic record
Abstract
The literature has shown that military members have serious psychological well-being (PWB) issues. However, very few studies have investigated PWB issues for Canadian military, especially employment equity (EE) groups, i.e., minorities. This study aims to investigate how job demands and resources are associated with PWB of Canadian military members and how burnout and affective commitment mediate these relationships. Additionally, this study examines whether there are differences between EE and non-EE groups. The data were retrieved from the 2022 CAF "Your Say Matters" survey. A total of 4,483 military members were used for our analysis. Using a structural equation modeling (SEM) approach, the mediational analysis was conducted via Mplus. Our findings showed most predictors were statistically significantly associated with PWB, and both burnout and affective commitment showed significant mediation effects. Compared to EE groups, non-EE males showed lower levels of psychological distress, job satisfaction and life satisfaction in general.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".