Out of uniform: psychosocial issues experienced and coping mechanisms used by Veterans during the military–civilian transition
Bibliographic record
Abstract
Introduction: The military–civilian transition is an important moment in the life course of Veterans. Collecting and interpreting data on psychosocial problems experienced during the transition make it possible to outline reintegration needs. Methods: A qualitative approach including semi-structured interviews was adopted. A total of 17 Veterans participated in the study. Participants speak French at home, live in Quebec, and were released from the Canadian Armed Forces (CAF) no more than five years before being interviewed. Results: Among participants released from the CAF for medical reasons, the main spheres of life in which problems occur are the medical / mental health, social, family, and personal spheres; among those released voluntarily, the main problem spheres are mental health, social, family, and financial. To remedy their psychosocial problems, the majority of participants relied on two coping mechanisms: social support and family support. Despite certain points of convergence, significant differences exist between the transitional course of Veterans released voluntarily and that of Veterans released for medical reasons,, which appears very arduous indeed. Discussion: This research enables a better understanding of the psychosocial problems experienced by Veterans during the transitional process and the coping mechanisms used to counter them.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".