The transition to civilian life in the context of chronic pain: Qualitative and exploratory research
Bibliographic record
Abstract
Introduction: The transition from military to civilian life can be a significant challenge for Canadian Armed Forces (CAF) Veterans, with approximately 39% experiencing difficulties. Veterans living with chronic pain face unique challenges during this transition, with impacts on their physical, mental, and social well-being. This study explores the experiences and needs of Veterans living with chronic pain during their transition to civilian life and examines the implications of chronic pain on various life domains. Methods: This qualitative and exploratory study investigated the effects of chronic pain on CAF Veterans' transitions to civilian life. Two three-hour focus groups were held with Veterans (N = 8) to explore their experiences with pain and its impacts on their social functioning during the transition to civilian life. Data were analyzed using thematic analysis. Results: Participants reported stigmatization of pain during military service, challenges in accessing civilian health care, significant emotional and relational impacts of chronic pain, and the importance of adaptive coping strategies. Participants also highlighted the need for peer support programs and specialized chronic pain clinics to facilitate the transition to civilian life and improve Veterans' quality of life. Discussion: Chronic pain significantly complicates CAF Veterans' transitions to civilian life by affecting various aspects of life and adjustment processes. The findings underscore the need for enhanced access to civilian health care and Veterans Affairs Canada services, along with preventive interventions, to support Veterans' well-being and facilitate successful transitions.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".