Characteristics of Canadian Armed Forces personnel open to alternatives to releasing from service
Bibliographic record
Abstract
Introduction: The Canadian Armed Forces (CAF) retention strategy seeks to instill a culture of retention where all members can thrive in fulfilling careers. Although the CAF promotes the use of alternatives to transitioning out of service (e.g., accommodations or alternative career options to influence members to remain in or return to service), not everyone contemplating release is willing to discuss and use these flexible retention options. Methods: , this study examined a wide range of work and organizational factors to determine correlates of willingness to be retained, by comparing members 1) intending to stay in the CAF (stayers), 2) intending to leave the CAF but willing to discuss retention options (leavers open to retention), and 3) intending to leave the CAF but not open to discussing retention options (other leavers). Results: Analyses of variance revealed that leavers willing to discuss retention options reported the highest levels of job stress and role overload, the lowest levels of satisfaction with relocation and family aspects of postings, and the highest levels of work-life and life-work conflict. They also reported higher job meaning, job engagement (physical, emotional, and cognitive), and affective commitment than other leavers, and similar levels of physical and cognitive engagement as stayers. Discussion: Results suggest that leavers open to retention may more easily be swayed by retention options that specifically target their work-related issues. Retaining these individuals would be beneficial for the CAF, given their high levels of engagement and organizational commitment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".