Mindfulness-Based Therapy for Military Populations with Chronic Pain: A Systematic Review
Bibliographic record
Abstract
Background: Due to the limited role of chronic pain medication in military personnel and the distress caused to the military population, mindfulness-based therapy has been considered for the follow-up treatment of military personnel with chronic pain. The purpose of this review is to explore the effect and the implementation of mindfulness-based therapy for the military population with chronic pain. Methods: The keywords for the search included “mindfulness” AND (“pain” OR “chronic pain”) AND (“military” OR “veteran”). The PubMed, Embase, and Cochrane Library databases were searched. The Cochrane Collaboration tool was used to independently assess the risk of bias of the included randomized controlled trials, and the Newcastle–Ottawa Scale was used to independently assess the risk of bias of the included case–control studies. Results: A total of 175 papers were identified; 65 duplicates were excluded, and 59 papers that did not meet the inclusion criteria were excluded after reading the titles and abstracts. The remaining 51 papers were read in full, 42 of which did not meet the inclusion criteria. Nine papers met the inclusion criteria and were included in the study. The nine studies included 507 veterans and 56 active-duty female military personnel. All pain interventions were mindfulness-based therapy, and all of them were integrated into or adapted from standard mindfulness courses. The results all showed that after mindfulness-based therapy, the relevant indicators improved. Conclusions: Mindfulness-based therapy is an effective treatment method for the military population with chronic pain. The review indicates that future research should focus on the best setting for mindfulness-based therapy, including the course content and time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".