Predictors of adjustment to life after service among Canadian military veterans
Bibliographic record
Abstract
The transition out of military service and into civilian life represents a considerable challenge for many military veterans. In this study we used mixture growth modeling and random forest analysis to examine predictors of adjustment to civilian life among recently released Canadian veterans (unweighted N = 455, weighted N = 11,100, weighted M age = 44.58, SD = 11.01). We used data from a national, longitudinal survey of Canadian military veterans, and examined 36 potential predictors of adjustment that included demographics, military characteristics/experiences, health behaviours, variables related to accessing care, social factors, psychological constructs, and physical health indicators. The results of mixture growth modelling revealed three distinct classes of adjustment following military release. Random forest analysis subsequently identified the most important predictors of adjustment (in order of importance), including life satisfaction, a sense of mastery, mental health, satisfaction with participants’ main activity (e.g., employed, retired), financial satisfaction, social support, general health, body mass index, age, and income. These predictors were used to examine differences among the latent classes. Our results revealed noteworthy differences between distinct classes of veterans, with regard to these predictor variables, the findings of which have the potential to inform targeted supports for veterans following release from 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".