Intangible rewards and the workplace well-being of civilian Department of National Defence employees
Bibliographic record
Abstract
Introduction: Since the onset of the COVID-19 global pandemic, individuals have had an opportunity to re-evaluate the role of work in their lives. Shifts in worker values offer employers the possibility of revisiting their offerings in ways that optimize employee experiences and well-being while also considering employee heterogeneity. These offerings typically include traditional compensation elements, as well as non-financial, or intangible, rewards. Positioning intangible rewards as job resources, the present study explored the moderating influences of employee characteristics on the relationships between these resources and workplace well-being among civilian employees of the Department of National Defence (DND). Methods: This study consisted of secondary analyses of the 2022 Public Service Employee Survey microdata, restricted to DND employees. Specifically, latent moderation analyses regressed emotional exhaustion and job satisfaction on employee characteristics (i.e., disability status, leader status, age), intangible rewards (e.g., support for flexible work arrangements [FWAs], a manageable workload), and their interactions. Results: Overall, higher levels of recognition were associated with reduced emotional exhaustion, and greater support for FWAs was related to increased job satisfaction. The remaining relationships were more nuanced. For instance, support for FWAs was a stronger protective factor of emotional exhaustion among persons with disabilities relative to persons without disabilities, and workload management was a stronger determinant of job satisfaction among leaders compared with non-leaders. Discussion: Notwithstanding its limitations, this study has preliminary implications for research and practice, especially with respect to the recruitment and retention of key workforce segments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".