Addiction Treatment and Telehealth: Review of Efficacy and Provider Insights During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Addiction treatment via telehealth expanded to unprecedented levels during the COVID-19 pandemic. This study aimed to clarify whether the research evidence on the efficacy of telehealth-delivered substance use disorder treatment and the experience of providers using telehealth during the pandemic support continued use of telehealth after the pandemic and, if so, under what circumstances. METHODS: Data sources included a literature review on the efficacy of telehealth for substance use disorder treatment, responses to a 2020 online survey from 100 California addiction treatment providers, and interviews with 30 California treatment providers and other stakeholders. RESULTS: Eight published studies were identified that compared addiction treatment via telehealth with in-person treatment. Seven found telehealth treatment as effective but not more effective than in-person treatment in terms of retention, therapeutic alliance, and substance use. One Canadian study found that telehealth facilitated methadone prescribing and improved retention. In the survey results reported here, California addiction treatment providers said that more than 50% of their patients were being treated via telehealth for intensive outpatient treatment, individual counseling, group counseling, and intake assessment. They were most confident that individual counseling via telehealth was as effective as in-person individual counseling and less sure about the relative effectiveness of telehealth-delivered medication management, group counseling, and intake assessments. CONCLUSIONS: Telehealth may help engage patients in addiction treatment by improving access and convenience. Additional research is needed to confirm that benefit and to determine how best to tailor telehealth to each patient's circumstances and with what mix of in-person and telehealth services.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".