Out of the shadows: Chronic pain in Canadian Armed Forces veterans — Proceedings of a workshop at the 2019 Forum of the Canadian Institute for Military and Veteran Health Research
Bibliographic record
Abstract
This commentary summarizes proceedings of a workshop on chronic pain in military personnel and veterans (released personnel) at the Annual Forum of the Canadian Institute for Military and Veteran Health Research in Gatineau and Ottawa on October 22, 2019. The extent and impact of chronic pain among Canadian Armed Forces (CAF) veterans and their families is significant and has been underappreciated, largely due to limited disclosure by serving and veteran military personnel, stemming from a fear of stigmatization. Living with pain is seen as a fact of life in military cultures, something to be endured and not discussed. Though progress is being made in reducing the stigma of mental illness, the discourse on chronic pain remains censored. This workshop's goal was to bring the discussion of chronic pain out of the shadows in the search for ways to help veterans and active service personnel living with chronic pain. Many points of view were brought forward at this first national Canadian multidisciplinary gathering of researchers, veterans with lived experience, clinicians, and policymakers. A CAF member described his lived experience with constant chronic pain. Clinicians described aspects of chronic pain in military personnel and veterans whom they treat in their clinics. Dr. Ramesh Zacharias described the new Chronic Pain Center of Excellence for Canadian Veterans that will be established with funding from Veterans Affairs Canada. Dr. Norman Buckley highlighted collaboration with the existing Chronic Pain Network funded by the Canadian Institute for Health Research. Audience members identified a diverse variety of issues.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.046 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 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".