Pain management program outcomes in veterans with chronic pain and comparison with nonveterans
Bibliographic record
Abstract
Background: In Canada, 41% of veterans experience chronic pain compared to the general population (20%). Many veterans with chronic pain also have comorbid disorders such as depression and posttraumatic stress disorder (PTSD), causing increased pain interference and disability.Aim: This study aims to investigate the effectiveness of a 4-week interdisciplinary pain management program at the Michael G. DeGroote Pain Clinic in Hamilton, Ontario, Canada, and to explore differences in pain experience and treatment outcomes between veterans and nonveterans in the program.Methods: Data were obtained from psychometric measures completed by 68 veterans and 68 nonveterans enrolled in the pain management program. By matching groups for age and gender, scores were compared between veterans and nonveterans. Outcomes investigated include catastrophizing, pain traumatization, stages of change, acceptance of pain, and program satisfaction. Multivariate analysis of variance (MANOVA) was conducted to examine session (admission–discharge) and group (veteran–nonveteran) differences, and independent t tests were used to examine differences in satisfaction measures.Results: Results showed that the program was effective for all participants, with significant differences between admission and discharge on several measures. However, veterans experienced significantly greater improvements in pain catastrophizing, kinesiophobia, pain traumatization, pain acceptance, stages of change, and pain coping, compared to nonveterans (P < 0.05). Though no significant differences in program satisfaction were found between groups, case managers evaluated veterans as having achieved greater benefits from the program.Conclusion: This study presents evidence supporting the effectiveness of an interdisciplinary pain management program in addressing pain-related variables in veterans and nonveterans and provides insight into how pain management is experienced differently by veterans.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".