Chronic pain and its management among Canadian veterans and non-veterans: digital health interventions as a viable solution
Bibliographic record
Abstract
Since the number of physically and mentally injured Canadian Veterans is very high, there is a growing need to support Veterans and assist them in coping with their challenges. Veterans are more prone to suffer from certain medical issues (chronic pain, post-traumatic stress disorder (PTSD)) that are related to their military experience. Veterans with chronic pain are more likely to have comorbid conditions, including mental health issues. They experience more disability from chronic pain and rates of PTSD incidence are 10 times greater than the general population. Cannabis is widely being utilized by Canadian veterans to treat chronic pain and other medical conditions. However, little research has been conducted to investigate patterns and effectiveness of cannabis usage, particularly among veterans.Published results have demonstrated that earlier OHPP (online health promotion program) versions can engage individuals to improve their HLH (healthy lifestyle habits) (exercise, stress management, healthy eating, etc.), provide measurable health benefits over up to 2 years (reduced stress, fatigue, insomnia, etc.) and reduce pain symptoms. Developing Innovative Digital Interventions to Manage Chronic Pain could be a viable solution for veterans suffering from chronic pain and comorbid mental health problems and may reduce or regulate medical cannabis (MC) consumption
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".