Investigating factors associated with medicinal cannabis authorization dosage among military Veterans in Canada
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».