Well-being among Canadian Armed Forces men and women: The roles of poor work-life balance and organizational support
Bibliographic record
Abstract
Introduction: The military and family can be demanding institutions with competing priorities that can cause poor work-life balance. This balance is crucial for organizational outcomes such as job engagement and burnout, and for managing psychological distress among military personnel. While organizational support improves these outcomes, support for families is less common and less studied. Additionally, work-life balance challenges may differ between men and women. This study examined the role of work-life balance and the protective roles of organizational support for members and families among Canadian Armed Forces (CAF) men and women. Methods: was administered to Regular Force CAF members (n = 4,349). Regression analyses examined the roles of poor work-life balance, including work-life conflict (work affecting life) and life-work conflict (life affecting work), and organizational support to members and families in predicting burnout, job engagement, and psychological distress. Results: Work-life and life-work conflicts predicted burnout and psychological distress, but not job engagement. Organizational support predicted all outcomes for CAF men and women and mitigated the impact of work-life conflict on burnout and psychological distress. However, support to families predicted job engagement and psychological distress among men only, while it moderated the impact of life-work conflict on psychological distress among men and work-life conflict on job engagement for both genders. Discussion: Work-life balance and organizational support for members and families can improve outcomes and well-being. Leaders should reinforce work-life balance and provide support to both members and families, recognizing gender dynamics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".