Attenuating chronic pain & trauma among naval special warfare veterans using psychoeducation
Bibliographic record
Abstract
Background & Purpose: Naval Special Warfare (NSW) veterans, including former Navy SEALs and Underwater Demolition Team Operators, have high rates of comorbid chronic pain and trauma. This DNP Project provided NSW veterans with access to a psychoeducation mobile application for chronic pain for 30 days to measure its effect on NSW veterans’ chronic pain and trauma scores. Methods: NSW veterans were recruited through an established NSW veteran’s organization. Nine subjects were enrolled, and eight subjects finished. An evidence-based, psychoeducation mobile application called Curable© was used for the 30-day intervention. The Short Form McGill Pain Questionnaire 2 and the Posttraumatic Stress Disorder for the Military Questionnaire were used to establish baseline and post-intervention pain and trauma scores. ☐ Results: Seven of the eight subjects experienced clinically significant decreases in chronic pain symptoms, and four of eight subjects experienced a clinically meaningful change in their trauma scores. Conclusion & Implications: The subjects broadly accepted the idea of an online-based, self-managed mobile application intervention for their chronic pain. More research is needed to establish if this intervention would have the same success in the wider NSW community. ☐ Keywords: Navy SEAL, Naval Special Warfare, chronic pain, trauma, psychoeducation, mobile application
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".