Access to MAT: Participants’ Experiences With Transportation, Non-Emergency Transportation, and Telehealth
Bibliographic record
Abstract
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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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".