Virtual Primary Care for People With Opioid Use Disorder: Scoping Review of Current Strategies, Benefits, and Challenges
Notice bibliographique
Résumé
BACKGROUND: There is a pressing need to understand the implications of the rapid adoption of virtual primary care for people with opioid use disorder. Potential impacts, including disruptions to opiate agonist therapies, and the prospect of improved service accessibility remain underexplored. OBJECTIVE: This scoping review synthesized current literature on virtual primary care for people with opioid use disorder with a specific focus on benefits, challenges, and strategies. METHODS: We followed the Joanna Briggs Institute methodological approach for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting our findings. We conducted searches in MEDLINE, Web of Science, CINAHL Complete, and Embase using our developed search strategy with no date restrictions. We incorporated all study types that included the 3 concepts (ie, virtual care, primary care, and people with opioid use disorder). We excluded research on minors, asynchronous virtual modalities, and care not provided in a primary care setting. We used Covidence to screen and extract data, pulling information on study characteristics, health system features, patient outcomes, and challenges and benefits of virtual primary care. We conducted inductive content analysis and calculated descriptive statistics. We appraised the quality of the studies using the Quality Assessment With Diverse Studies tool and categorized the findings using the Consolidated Framework for Implementation Research. RESULTS: Our search identified 1474 studies. We removed 36.36% (536/1474) of these as duplicates, leaving 938 studies for title and abstract screening. After a double review process, we retained 3% (28/938) of the studies for extraction. Only 14% (4/28) of the studies were conducted before the COVID-19 pandemic, and most (15/28, 54%) used quantitative methodologies. We summarized objectives and results, finding that most studies (18/28, 64%) described virtual primary care delivered via phone rather than video and that many studies (16/28, 57%) reported changes in appointment modality. Through content analysis, we identified that policies and regulations could either facilitate (11/28, 39%) or impede (7/28, 25%) the provision of care virtually. In addition, clinicians' perceptions of patient stability (5/28, 18%) and the heightened risks associated with virtual care (10/28, 36%) can serve as a barrier to offering virtual services. For people with opioid use disorder, increased health care accessibility was a noteworthy benefit (13/28, 46%) to the adoption of virtual visits, whereas issues regarding access to technology and digital literacy stood out as the most prominent challenge (12/28, 43%). CONCLUSIONS: The available studies highlight the potential for enhancing accessibility and continuous access to care for people with opioid use disorder using virtual modalities. Future research and policies must focus on bridging gaps to ensure that virtual primary care does not exacerbate or entrench health inequities.
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 | 0,043 | 0,135 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,024 | 0,021 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».