Digital geographies of care: Telehealth landscapes of addiction treatment during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has created new digital health care landscapes for the management of substance use and misuse. While telehealth was prohibited for addiction treatment prior to the pandemic, the severity of COVID-19 precipitated telehealth expansion for the delivery of individual and group-based treatment. Research has highlighted benefits and challenges of telehealth; however, little is known about the impacts of telehealth on the quality, use, and effectiveness of treatment. Fewer studies examine how these emerging digital geographies of care transform the spaces and landscapes of substance misuse. This article examines how telehealth affects landscapes of opioid use disorder care in Pennsylvania, West Virginia, and Kentucky during the COVID-19 pandemic. Our findings reveal that while telehealth extends access to treatment for opioid use disorder (OUD), it also creates new care inequities within and between providers and clientele that can undermine effective care and recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".