Understanding the use of telemedicine across different opioid use disorder treatment models: A scoping review
Bibliographic record
Abstract
IntroductionThe COVID-19 pandemic has instigated the development of telemedicine-mediated provision of medications for opioid use disorder such as buprenorphine and methadone, referred to as TMOUD in this study. As services start to return to pre-pandemic norms, there is a debate around the role of TMOUD as addition to or replacement of the conventional cascade of care for people with opioid use disorder (PWOUD). This scoping review is designed to characterize existing TMOUD services and provide insights to enable a more nuanced discussion on the role of telemedicine in the care of PWOUD.MethodsThe literature search was conducted in OVID Medline, CINAHL, and PsycINFO, from inception up to and including April 2023, using the Joanna Briggs Institute methodology for scoping reviews. The review considered any study design that detailed sufficient descriptive information on a given TMOUD service. A data extraction form was developed to collect and categorize a range of descriptive characteristics of each discrete TMOUD model identified from the obtained articles.ResultsA total of 45 articles met the inclusion criteria, and from this, 40 discrete TMOUD services were identified. In total, 33 services were US-based, three from Canada, and one each from India, Ireland, the UK, and Norway. Through a detailed analysis of TMOUD service characteristics, four models of care were identified. These were TMOUD to facilitate inclusion health, to facilitate transitions in care, to meet complex healthcare needs, and to maintain opioid use disorder (OUD) service resilience.ConclusionsCharacterizing TMOUD according to its functional benefits to PWOUD and OUD services will help support evidence-based policy and practice. Additionally, particular attention is given to how digital exclusion of PWOUD can be mitigated against.
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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 | 0.026 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 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".