Shared decision-making and client-reported dose satisfaction in a longitudinal cohort receiving injectable opioid agonist treatment (iOAT)
Notice bibliographique
Résumé
BACKGROUND: Across different types of oral Opioid Agonist Treatment for people with Opioid Use Disorder, receiving a dose that meets their needs is associated with better outcomes. Evidence also shows patients are more likely to receive an "adequate dose" when their prescribers are involving them in decision making. Neither of these findings have been studied in the context of injectable Opioid Agonist Treatment, which is the purpose of this study. METHODS: This study was a retrospective analysis of an 18-month prospective longitudinal cohort study of 131 people receiving injectable Opioid Agonist Treatment. In the 18-month study, observations were collected every two months for one year, and then once more at 18 months. At 6 months, participants were asked whether their dose was satisfactory to them (outcome variable). Generalized Estimating Equations were used, to account for multiple observations from each participant. The final multivariate model was built using a stepwise approach. RESULTS: Five hundred forty-five participant-observations were included in the analysis. Participant-observations were grouped by "dose is satisfactory" and "wants higher dose". From unadjusted analyses, participants were less likely to report being satisfied with their dose if they: were Indigenous, had worse psychological or physical health problems, had ever attempted suicide, were younger when they first injected any drug, were a current smoker, felt troubled by drug problems, gave their medication a lower "drug liking" score, and felt that their doctor was not including them in decisions the way they wanted to be. In the final multivariate model, all previously significant associations except for "current smoker" and "troubled by drug problems" were no longer significant after the addition of the "drug liking" score. CONCLUSIONS: Patients in injectable Opioid Agonist Treatment who are not satisfied with their dose are more likely to: be troubled by drug problems, be a current smoker, and report liking their medication less than dose-satisfied patients. Prescribers' practicing shared decision-making can help patients achieve dose-satisfaction and possibly alleviate troubles from drug problems. Additionally, receiving a satisfactory dose may be dependent on patients being able to access an opioid agonist medication (and formulation) that they like.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».