An application of the biopsychosocial model for pain in Canadian Veterans Life After Service Studies 2019 survey
Bibliographic record
Abstract
Introduction: Chronic pain is highly prevalent and disruptive to the general population. Relative to civilians, Veterans are more likely to experience chronic pain due to occupational requirements during military service and associated psychological difficulties. The biopsychosocial model is a promising theoretical framework for understanding potential causes of chronic pain, as well as a path for implementing evidence-based interventions. Methods: This study applied the biopsychosocial framework to data from the Life After Service Studies 2019 survey by modelling biopsychosocial predictors of chronic pain and controlling for relevant participant and military demographics. The use of structural equation modelling solutions allowed the biopsychosocial model to be assessed simultaneously as a model with latent variables, rather than traditional regression or logistic analyses. Results: Biological factors and a combined psychological and social factor were related to an increased likelihood of having chronic pain. The three-model solution showed anxiety, posttraumatic stress disorder, suicidal ideation, and life stress were positively associated, while hypervigilance, alcohol use, social support, and labour force status was negatively related to the latent variable. Discussion: Results showed specific biopsychosocial factors were related to chronic pain through different structural equation modelling solutions. Understanding the contribution of biopsychosocial factors can inform evidence-based interventions and policy changes through multidisciplinary approaches.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".