Employment outcomes among transitioned Australian Defence Force members: An exploration of sex differences
Bibliographic record
Abstract
Introduction: Transitioning to civilian life is a challenging period of adjustment for military personnel. Gaining civilian employment after leaving the military has several benefits for mental and physical health, yet it is one of many challenges Veterans face. Females are largely under-represented in Veteran unemployment research despite having unique experiences during service that may affect employment after transition. Methods: This secondary data analysis of a sub-sample of males and females who recently transitioned out of the Australian Defence Force explores sex differences in terms of transition, service, and individual factors associated with unemployment. Results: Findings showed female unemployment was greater among those with children, who transitioned at a younger age, and who lived in stable housing, which may be explained by primary caregiving responsibilities. Age at time of transition, having children, and living in stable housing differentially affected unemployment among females and males, whereas number of years served, level of psychological distress, number of recent life events, transition status, and discharge reason were shown to affect employment outcomes for females and males alike. Factors found to be unequally associated with employment outcomes for males were time since transition, service type, and level of education. Discussion: Findings indicate that employment outcomes are associated with varied factors for females and males. Further research is needed to develop greater awareness of female transition experiences to ensure that services support the unique needs of females leaving the military.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".