Characteristics of Canadian Veterans reimbursed for cannabis for medical purposes: Life After Service Survey 2016
Bibliographic record
Abstract
LAY SUMMARY This research explored the demographic, military service, and health characteristics associated with cannabis for medical purposes (CMP) reimbursements among Veterans Affairs Canada (VAC) clients and respondents of the Life After Service Survey 2016 (LASS). Of the initial number of indicators selected contained in the LASS 2016, some specific variables were significantly associated with CMP reimbursement, from which physical/mental health and well-being indicators, such as anxiety, posttraumatic stress disorder (PTSD), depression, bowel ulcer, traumatic brain injury, chronic pain, needing help with tasks, psychological distress, and having three or more conditions of the PTSD diagnosis, were positively associated with CMP. Moreover, unemployment, having low income (< $5,000), a difficult adjustment, being very dissatisfied with life, having low social support, a weak community belonging, and reporting high stress also increased the odds of being reimbursed. These results will help to identify a preliminary profile of VAC clients with higher need for CMP reimbursement.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".