Transition Needs Among Veterans Living With Chronic Pain: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: A third of Canadian Armed Forces veterans report difficulty adjusting to post-military life. Moreover, an estimated 40% of Canadian veterans live with chronic pain, which is likely associated with greater needs during the transition from military to civilian life. This review explores challenges and transition needs among military personnel living with chronic pain as they return to civilian life. METHODS: We searched MEDLINE, EMBASE, CINAHL, Scopus, and Web of Science from inception to July 2022, for qualitative, observational, and mixed-method studies exploring transition needs among military veterans released with chronic pain. Reviewers, working independently and in duplicate, conducted screening and used a standardized and pilot-tested data collection form to extract data from all included studies. Content analysis was used to create a coding template to identify patterns in challenges and unmet needs of veterans transitioning to civilian life, and we summarized our findings in a descriptive manner. RESULTS: Of 10,532 unique citations, we identified 43 studies that reported transition challenges and needs of military personnel; however, none were specific to individuals released with chronic pain. Most studies (41 of 43; 95%) focused on military personnel in general, with one study enrolling individuals with traumatic brain injury and another including homeless veterans. We identified military-to-civilian challenges in seven areas: (1) identity, (2) interpersonal interactions/relationships, (3) employment, (4) education, (5) finances, (6) self-care and mental health, and (7) accessing services and care. CONCLUSIONS: Military personnel who transition to civilian life report several important challenges; however, the generalizability to individuals released with chronic pain is uncertain. Further research is needed to better understand the transition experiences of veterans with chronic pain to best address their needs and enhance their well-being.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".