Access to MAT: Participants’ Experiences With Transportation, Non-Emergency Transportation, and Telehealth
Notice bibliographique
Résumé
INTRODUCTION: Access to medication assisted treatment (MAT) for opioid use disorder (OUD) in the United States is a significant challenge for many individuals attempting to recover and improve their lives. Access to treatment is especially challenging in rural areas characterized by lack of programs, few prescribers, and transportation barriers. This study aims to better understand the roles that transportation, Medicaid-funded non-emergency medical transportation (NEMT), and telehealth play in facilitating access to MAT in West Virginia (WV). METHODS: We developed this survey using an exploratory sequential mixed methods approach following a review of current peer-reviewed literature plus information gained from 3 semi-structured interviews and follow-up discussions with 5 individuals with lived experience in MAT. Survey results from 225 individuals provided rich context on the influence of transportation in enrolling and remaining in treatment, use of NEMT, and experiences using telehealth. Data were collected from February through August 2021. RESULTS: We found that transportation is a significant factor in entering into and remaining in treatment, with 170 (75.9%) respondents agreeing or strongly agreeing that having transportation was a factor in deciding to go into a MAT program, and 176 (71.1%) agreeing or strongly agreeing that having transportation helps them stay in treatment. NEMT was used by one-quarter (n = 52, 25.7%) of respondents. Only 13 (27.1%) noted that they were picked up on time and only 14 (29.2%) noted that it got them to their appointment on time. Two thirds of respondents (n = 134, 66.3%) had participated in MAT services via telehealth video or telephone visits. More preferred in-person visits to telehealth visits but a substantial number either preferred telehealth or reported no preference. However, 18 (13.6%) reported various challenges in using telehealth. CONCLUSIONS: This study confirms that transportation plays a significant role in many people's decisions to enter and remain in treatment for OUD in WV. Additionally, for those who rely on NEMT, services can be unreliable. Finally, findings demonstrate the need for individualized care and options for accessing treatment for OUD in both in-person and telehealth-based modalities. Programs and payers should examine all possible options to ensure access to care and recovery.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 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 | 0,002 |
| 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 ».