Qualitative Inquiry on the Health and Well-Being of Canadian Armed Forces Members and Veterans during Medical Release
Bibliographic record
Abstract
Introduction The transition from military to civilian life can be a difficult adjustment, particularly for those members who have medically released. However little research has been conducted to gain a nuanced understanding of the experiences of ill and/or injured Canadian members and veterans throughout the transition period.Methodology Forty-five semi-structured interviews were conducted to gain insight on the challenges that medically releasing Canadian Armed Forces (CAF) members (N = 14) and medically released veterans (N = 31) encountered during their transition process. Topics explored their current health and well-being, as well as transition stressors and challenges experienced during and post-release. Transcripts of interviews were subjected to a thematic analysis.Results Findings demonstrated that numerous ill and injured members experienced both physical and mental health challenges, which caused significant stress and impacted their psychological well-being. The present study also highlighted the stress and challenges that participants experienced both during and after release. Common themes found for medically-releasing members were: (1) uncertainty, (2) transition process and CAF support, and (3) lack of readiness. Veterans’ most common stressors related to: (1) managing their illness and/or injury, (2) managing employment, (3) pensions and disability support, and (4) finding meaning and purpose.Discussion Recommendations and implications regarding improving veteran well-being, as well as the implementation and development of various types of services and programs are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".