Research Priorities Among Canadian Military Veterans Living With Chronic Pain: A Cross-Sectional Survey
Bibliographic record
Abstract
INTRODUCTION: Chronic pain is a debilitating problem that disproportionately affects military veterans. We completed a qualitative study that identified 20 research priorities of Canadian veterans living with chronic noncancer pain. The aim of this study was to establish the generalizability of these priorities. MATERIALS AND METHODS: From January to March 2021, we emailed a 45-item survey to a list of Canadian veterans living with chronic noncancer pain that asked about the relative importance of 20 research priorities. RESULTS: Overall, 313 of 701 Canadian military veterans living with chronic noncancer pain returned a completed survey (45% response rate). All 20 research priorities listed in the survey were endorsed by ≥75% of respondents, and four received ≥95% endorsement: (1) optimizing chronic pain management after release from the military; (2) establishing the effectiveness of self-care; and (3) identifying and (4) treating mental illness among veterans living with chronic pain. One research priority differed significantly by gender; 50% more females than males rated improving chronic pain care while in the military as important (99% vs. 49%, P < .001). CONCLUSIONS: Our survey established research priorities among Canadian veterans living with chronic noncancer pain. These findings should be considered by granting agencies when formulating calls for proposals and by researchers who wish to undertake research that will address the needs of military veterans living 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".