Burnout, morale, and psychological safety among designated group members in Canada’s military
Bibliographic record
Abstract
Introduction: There is evidence that designated group members (DGMs; women, Indigenous individuals, persons with disabilities [PwDs], and racialized people) experience unique challenges within the Canadian Armed Forces (CAF), including experiencing microaggressions and feeling less included than non-DGMs, which can impact well-being. Therefore, this article focuses on the experiences and perceptions of CAF DGMs in terms of key workplace outcomes (morale, burnout) and perceptions of workplace psychological safety. Methods: ) were analyzed on a sample of 4,483 CAF Regular Force members. Analyses of variance (ANOVAs) were conducted to identify key differences between the four designated groups and a fifth group (those who were not DGMs; i.e., everyone else) on workplace outcomes (morale and burnout), and perceptions of psychological safety. Results: Both PwDs and Indigenous individuals appeared to fare worse than all other groups. Specifically, PwDs scored lower on morale and psychological safety and higher on burnout than all groups, except for Indigenous individuals. Indigenous individuals scored lower on morale than women who were not part of another group and racialized people, and higher on burnout than racialized people, who experienced less burnout than everyone else. Discussion: These findings highlight the importance of examining the unique experiences of DGMs with respect to their workplace well-being. This research contributes to reconstitution efforts aimed at increasing recruitment and retention by shedding light on differences in key workplace well-being outcomes and perceived psychological safety among DGMs in the CAF.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".