Participation in occupations, health and adjustment during the transition from military service: A cross-sectional study
Bibliographic record
Abstract
Participation in meaningful occupations is central to health, well-being, and adjustment during the transition from military service. The aim of the present study was to identify what occupations transitioning Australian Defence Force members participate in for the purpose of improving their health and well-being. A secondary aim was to identify if participation in various occupations was associated with better self-reported health and/or adjustment outcomes. One hundred and ninety-eight former Australian Defence Force members discharged on or after January 1, 2004 responded to a cross-sectional survey measuring adjustment, physical and mental health, and participation in occupations. Occupations were coded using the Time Use Classification system developed by the Australian Bureau of Statistics. An easier adjustment was reported by former service members who participated in employment-related activities, domestic activities, voluntary work and care activities, and social and community interaction (MD = −0.63 to −0.45, d = .37 to .52). Participation in employment-related activities, social and community interaction, and sport and outdoor activity was associated with better physical health (MD = 3.20 to 3.73, d = .34 to .40). Participation in employment-related activities was also associated with better mental health (MD = −3.75, d = .54). This research indicates that participation in occupation is a factor that may be utilized with former service members to positively influence health and adjustment during military transitions. Given differences in participation and outcomes among different sub-groups, it is recommended that occupation-based programs be tailored to individual preferences and transition needs.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".