Factors associated with work satisfaction among Veterans
Bibliographic record
Abstract
Introduction: Work satisfaction contributes to the health and well-being of Veterans transitioning to civilian life after service. Therefore, the purpose of this study is to examine the factors contributing to work satisfaction among Canadian Veterans. Methods: We examine multiple factors thought to be associated with work satisfaction using the cross-sectional 2010 survey of the Life After Service Studies (LASS) program of research, which includes a national sample of 3,154 Canadian Veterans released from the regular forces between 1998 and 2007. We performed both unadjusted and adjusted logistic regression models for three rank groupings: privates and cadets, Non-Commissioned Members (NCMs), junior and senior, and officers (junior and senior). Results: In the year after release, 73% of Veterans were satisfied with their work. Officers (89%) were the most satisfied, followed by NCMs (77%), and privates and cadets (52%). Using unadjusted models, factors associated with higher satisfaction rates were: release due to reaching retirement age; being older than 50; reporting no work stress; reporting knowledge, skills, and ability transferability; and similar job tasks between military and civilian occupations; reporting easy adjustment to civilian life; and being satisfied with finances. Using adjusted models, only satisfaction with finances was associated with work satisfaction for NCMs, privates, and cadets. Only involuntary release and years of service were associated with work satisfaction for officers. Discussion: Work satisfaction rates varied considerably according to military rank, and were highest among officers and lowest among privates and cadets. This suggests a need to account for rank when developing strategies to improve work satisfaction rates and reintegration into civilian life among Veterans.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".