Cluster analysis of Canadian Armed Forces veterans living with chronic pain: Life After Service Studies 2016
Bibliographic record
Abstract
Objective: This study explored the heterogeneity of Canadian Armed Forces veterans living with chronic pain to inform service needs planning and research using cluster analysis.Design: We used a national cross-sectional Statistics Canada population survey.Participants: Participants included 2754 Canadian Armed Forces (CAF) Regular Force veterans released from service between 1998 and 2015 and surveyed in 2016.Methods: We used cluster analysis of veterans with chronic pain based on pain severity, mental health, and activity limitation characteristics. We compared clusters for sociodemographic, health, and service utilization characteristics.Results: Of 2754 veterans, 1126 (41%) reported chronic pain. Veterans in cluster I (47%) rarely had severe pain (2%) or severe mental health problems (8%), and none had severe activity limitations. Veterans in cluster II (26%) more often than veterans in cluster I but less often than veterans in cluster III endorsed severe pain (27%) and severe mental health problems (22%) and were most likely to report severe activity limitation (91%). Veterans in cluster III (27%) were most likely to report severe pain (36%) and severe mental health problems (96%), and a majority reported severe activity limitations (72%). There was evidence of considerable heterogeneity among individuals in terms of socioeconomic characteristics, pain characteristics, mental and physical health status, activity limitations, social integration, and service utilization indicators.Conclusions: About half of Canadian veterans living with chronic pain infrequently endorse severe pain or serious mental health issues without severe activity limitations. The other half had more complex characteristics. The heterogeneity of CAF veterans with chronic pain emphasizes the need for support systems that can address variability of needs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".