Comparing Work-Related Correlates of Life Satisfaction for Combat Versus Non-Combat Military Veterans
Bibliographic record
Abstract
Military veterans (n = 153) completed an on-line survey and were broken down into combat (n = 92) versus non-combat (n = 61) veterans. The combat veterans had higher life satisfaction, perceived occupational alternatives, and education level versus the non-combat veterans. Looking at correlates to life satisfaction, for both samples number of prior traumatic events (negative) and personal accomplishment (positive) were significantly related. In addition, for the combat veterans, highest education level and perceived occupational alternatives were significantly related to life satisfaction. A high percentage of both combat and non-combat veterans were currently going to school to further their education. Working with employed combat and non-combat veterans, those currently going to school to further their education had higher perceived occupational alternatives than veterans not going back. Working with a smaller group of combat versus non-combat respondents who did volunteer work, the non-combat veterans were higher on perceived meaningfulness of volunteer work than the combat veterans. Returning to school can be one way to help military veterans find rewarding meaningful work, through perceived occupational alternatives, which can help to increase their life satisfaction. If increased education is not an option, volunteer work may also lead to higher perceived meaningful work. As veterans transition from military to civilian life, military out-processing should continue to counsel/prepare transitioning veterans for: finding/interviewing for jobs as well as realistic new careers; identifying meaningful voluntary work opportunities; or finding resources for furthering one’s education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".