Non-pharmacological interventions for veterans living with chronic pain: a scoping review and intervention map
Bibliographic record
Abstract
PURPOSE: To describe the content of studied intervention programs, the rationale/mechanism and outcomes from these studies and the limitations and gaps within the existing literature. METHODS: CINAHL, EMBASE, PubMed, Cochrane Reviews, and Google Scholar were searched for studies. Three authors screened studies against predefined inclusion criteria. Data were analyzed through qualitative synthesis. Articles were included if they addressed a rehabilitation intervention that focused on chronic pain management in a population of military veterans. RESULTS: A total of 31 articles were included, 25 being randomized trials. Interventions varied in design components, sessions, and delivery mode (58% in-person, 25% remote, 17% mixed). Adherence and fidelity were reported by 78% of studies. Only 4/31 studies reported the use of veteran engagement during development of the intervention. A conceptual map summarizing the intervention components (5 main categories) expected mechanisms and outcomes (process, health and patient specific) from the primary interventions was created. CONCLUSIONS: Poor reporting of content, rationale, and frameworks of non-pharmacological interventions for military veterans may explain why systematic reviews have not found support for their value. Future trials must improve rigor in design and reporting to be explicit and responsive to the needs of the veteran population facing chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".