Investigating factors associated with medicinal cannabis authorization dosage among military Veterans in Canada
Bibliographic record
Abstract
LAY SUMMARY This work investigated factors associated with medicinal cannabis authorization dosage among 9,104 Canadian Armed Forces Regular Force Veterans in Canada with a valid Cannabis for Medicinal Purposes reimbursement on Dec. 31, 2020, and identified various socio-demographic, Veterans Affairs Canada (VAC) pensionable conditions, and military service characteristics associated with higher-dose medicinal cannabis authorizations. Among those with higher dose reimbursements were Veterans under the age of 30 years, males, those receiving benefits for health conditions (e.g., hearing loss, musculoskeletal, or mental health conditions), those participating in VAC rehabilitation services, those with an earlier year of reimbursement, those who were released involuntarily from service, and those indicating land military environment service at date of release. In statistical models investigating the impact of multiple factors, some of the strongest associations with higher dosages were observed for Veterans with mental health conditions, those with earlier reimbursements, and province of residence. Introduction: Since 2008, Veterans Affairs Canada (VAC) has provided Canadian Armed Forces Regular Force Veterans with reimbursement of Cannabis for Medical Purposes (CMP) authorizations. The authorized dosage and authorization criteria have changed with time. This study investigated factors associated with CMP authorizations and dosage among CMP-authorized Veterans. Methods: CMP authorizations among 9,104 Veterans residing in Canada on Dec. 31, 2020, were linked with VAC reimbursement, VAC client, and military personnel records. Multivariable logistic regression models were used to examine relationships between CMP dosage and socio-demographic, health, and military characteristics. Results: Among Veterans with CMP authorizations, the strongest associations with a larger authorization dosage (4–10 grams vs. 1–3 grams) were observed for Veterans receiving benefits for mental health conditions in combination with other health conditions (OR = 3.47 compared with those with no mental health conditions). A larger authorization dosage was associated with province of residence (OR = 3.36 for New Brunswick compared with Ontario), earlier year of authorization (OR = 2.19 for 2014) compared with 2016, being male (OR = 1.68), active participation in a rehabilitation program (OR = 1.45), land environment at the time of release from military service (OR = 1.24) compared with air environments, and involuntary release from service (OR = 1.65) and medical release (OR = 1.11) compared with voluntary release. Discussion: Factors associated with larger CMP authorization dosage among military Veterans in Canada appeared multifactorial, spanning socio-demographic, health, and military characteristics. This complexity should be considered by treatment providers and clinicians working with military Veterans.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".