Navigating transition: A mixed methods study protocol for understanding chronic pain, resilience, and well-being among Canadian Armed Forces Veterans, families, and service providers
Bibliographic record
Abstract
Abstract Introduction Transitioning from military to civilian life is often challenging for Canadian Armed Forces (CAF) Veterans, and chronic pain further complicates reintegration and wellbeing. Family members and service providers play critical but understudied roles in this process. Methods This mixed-methods study combines secondary analysis of the previous dataset with new qualitative interviews. Quantitative analyses will describe characteristics of Veterans with chronic pain and assess associations between pain severity and transition outcomes using regression models. Qualitative interviews with Veterans (n=10), family members (n=10), and service providers (n=10) will explore pain management, barriers and facilitators to care, and resilience strategies, analyzed using both deductive (framework-guided) and inductive thematic approaches. Results Findings will identify correlates of chronic pain and transition outcomes, as well as systemic gaps and resilience-enabling supports across the transition continuum. Discussion This study is the first in Canada to examine chronic pain in Veterans’ transition through a multi-system lens inclusive of family and provider perspectives. Grounded in resilience frameworks, the design addresses individual, relational, and structural influences on post-service adjustment. Anticipated findings will inform targeted interventions, caregiver supports, and provider education to strengthen resilience and improve care coordination for Veterans with chronic pain.
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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 | 0.089 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".