An exploration of the increasing prevalence of chronic pain among Canadian veterans: Life After Service Studies 2016 and 2019
Bibliographic record
Abstract
The Life After Service Study (LASS) suggests that the absolute prevalence of chronic pain among Canadian veterans, defined as pain lasting 3 months or longer, increased by 10% from 2016 to 2019. We explored the association of year of survey administration, sociodemographic characteristics, military service, and health-related factors with the prevalence of chronic pain among Canadian veterans. We analyzed 2016 and 2019 LASS data and built a multivariable regression model to explore factors associated with chronic pain. Measures of association are reported as adjusted odds ratios (ORs) and absolute risk increases (ARIs). The 2016 LASS (73% response rate; 3002 of 4121) reported a 41.4% prevalence of chronic pain, and the 2019 LASS (72% response rate; 2630 of 3671) reported a 51.5% prevalence of chronic pain among Canadian veterans. Respondents who completed the 2019 LASS were more likely to endorse an anxiety or related disorder, mood disorder, probable posttraumatic stress disorder, and traumatic brain injury. In our adjusted regression model, year of survey administration was not associated with chronic pain (OR = 1.08, P = 0.8); however, we found large associations with obesity class 1 (body mass index [BMI] = 30.0–34.9; OR = 3.66; 95% confidence interval [CI] 1.46–9.17; ARI 27%), obesity class 2 (BMI = 35.0–39.9; OR = 8.10; 95% CI 1.67–39.3; ARI 47%), mood disorder (OR = 3.20; 95% CI 1.49–6.88; ARI 24%), and an anxiety or related disorder (OR = 4.53; 95% CI 1.28–16.0; ARI 33%). The increase in chronic pain among Canadian veterans from 2016 to 2019 appears confounded by increased comorbidities associated with chronic pain among responders in 2019.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".