Well-being of Veterans with chronic pain with fewer activities limited by pain: Life After Service Survey 2019
Bibliographic record
Abstract
Introduction: There is considerable variance in the relationship between pain severity and activities limited by pain in Canadian Armed Forces (CAF) Veterans. It is possible those who reported fewer activities with pain may be doing better in certain well-being domains. Understanding these relationships may guide future research and clinical care. This article reports the prevalence of chronic pain in CAF Veterans, describes their characteristics, and identifies well-being indicators associated with fewer activities limited by pain. Methods: The Life After Service Survey (LASS) 2019 survey was a Statistics Canada cross-sectional, computer-assisted telephone interview survey of the well-being of CAF Regular Force Veterans released in 1998-2018. Data were analyzed using descriptive analysis and ordinal logistic regression modelling. Results: There were 1,222 Veterans who reported living with chronic pain, with a prevalence of 50.7% (95% CI, 48.0-53.4). Those with moderate or severe pain who reported no or few activities limited by pain were more likely to be ages 50-59 years, female, and employed, and more likely to have higher education, higher rank, longer length of service, or higher income adequacy. They were also more likely to be satisfied with family, main activities, or finances. Regression modelling in those with moderate to severe pain demonstrated those with fewer activities limited by pain were less likely to have low mastery (unadjusted odds ratio = 18.5; 95% CI, 8.0-42.6). Discussion: The strong association between a sense of mastery and fewer activities limited by pain provides important avenues for future research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".