Canadian Veterans’ Experiences of Living with Chronic Pain: A Descriptive Qualitative Study
Bibliographic record
Abstract
Background: An estimated 30% of veterans live with chronic pain, compared to 20% of Canadians in the general population. Veterans face health care challenges upon release from the military, increasing difficulties in obtaining chronic pain care. Aims: We explored experiences of Canadian Armed Forces veterans living with chronic pain, their transition from military to civilian care, perceived barriers and facilitators to chronic pain care, and impacts of their pain on the domains of well-being. Methods: We conducted a qualitative descriptive study using semistructured interviews. We used a deductive/inductive approach to derive themes and concepts from interview transcripts. Results: Thirty-five veterans living with chronic pain participated. Participants reported that pain affected their lives in numerous ways, including negatively impacting relationships and limiting activities of daily living and leisure. They identified barriers to care, including lack of access to family doctors or health care services, reluctance to ask for help, and challenges in obtaining coverage for services from Veterans Affairs Canada. Facilitators included support from other veterans and online resources. Chronic pain had bidirectional effects on domains of well-being. Conclusions: Experiences of pain varied among Canadian veterans, and military culture played a role in perceptions and management of pain. Barriers and facilitators to chronic pain care were highlighted from their time in the military into their transition to civilian care. Participants described the impact of chronic pain on their overall well-being. Determining whether these findings are relevant to a larger population of Canadian veterans will be important for future research and knowledge translation to improve chronic pain care for Canadian veterans.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".