Treatment preferences among Canadian military Veterans with chronic low back pain: Mixed-methods cross-sectional survey
Bibliographic record
Abstract
Introduction: Patients are more willing to initiate and engage in treatments they are predisposed toward; however, Canadian military Veterans' preferences for managing low back pain are uncertain. This study examined Canadian military Veterans' use of, and preferences for, health care providers for managing chronic low back pain, both while serving and after release. Methods: A 33-item survey was emailed, in English and in French, to 1,632 Canadian Armed Forces (CAF) Veterans in February-May 2023. CAF Veterans living with chronic low back pain were eligible to complete the survey, which asked about demographic variables, military service, chronic-low-back-pain-related characteristics, and experiences and attitudes toward health care providers and therapeutic approaches to chronic low back pain. Results: Of 1,632 individuals, 290 returned a completed survey (18% response rate). Almost all (98%) who responded reported living with chronic low back pain for more than 5 years, and 91% indicated first experiencing low back pain during military service. Among 12 health care provider options for managing chronic low back pain, respondents most preferred massage therapists, physiotherapists, family physicians, and chiropractors. The most-attended off-base practitioners for low back pain while serving in the military were physiotherapists (39%), chiropractors (35%), and registered massage therapists (30%). Most respondents endorsed that registered massage therapy (70%), physiotherapy (60%), chiropractic care (51%), and occupational therapy (50%) should be available on base for serving military personnel. Discussion: Findings suggest there may be opportunities to better align on-base health care for low back pain with military personnel's evidence-based treatment preferences.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".