Associations of chronic pain and PTSD factors among military personnel: An exploration of the mutual maintenance model
Bibliographic record
Abstract
Introduction: Chronic pain is common among Canadian Armed Forces (CAF) service members and Veterans. This has prompted investigators to develop theoretical models that identify the factors contributing to chronic pain. The mutual maintenance model (MMM) posits that when chronic pain co-occurs with posttraumatic stress disorder (PTSD), seven cognitive, behavioural, and affective features of both disorders work to maintain PTSD and chronic pain. This study examined the MMM by investigating which model factors predicted the presence of chronic pain and PTSD and which PTSD symptom clusters predicted chronic pain severity and pain interference in a sample of 233 CAF service members and Veterans. Methods: Participants completed an online survey assessing PTSD and chronic pain symptoms and proxies for MMM factors. Two binary logistic regression analyses determined which MMM factors predicted the presence of chronic pain and PTSD. Two multiple linear regressions determined which PTSD symptom clusters predicted chronic pain severity and interference. Results: Intrusion symptoms were associated with the presence of chronic pain, and anxiety symptoms were associated with the presence of PTSD. The hyper-arousal symptom cluster was positively related to pain severity and interference. Discussion: Contrary to the predictions of the MMM, only intrusion and anxiety symptoms were associated with chronic pain and PTSD presence, respectively. Only hyper-arousal was associated with pain severity and interference. Although cross-sectional analyses cannot demonstrate causation, results of the study suggest that three of the seven MMM factors (i.e., intrusion and anxiety symptoms and hyper-arousal) maintain or exacerbate chronic pain when it co-occurs with PTSD.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".