Impact of COVID-19–related Isolation on Individuals in Treatment for Substance Use
Bibliographic record
Abstract
Background For individuals in treatment for substance use, supportive social networks are essential to protect against a return to use. Objective This study aimed to explore the impact of the swift and severe isolation brought on by the COVID-19 pandemic, specifically for individuals in treatment for substance use disorder, by exploring the relationships amongst social connectedness and isolation to treatment accessibility, mental health, and substance use. Methods A total of 24 semistructured interviews were conducted from May 2020 to August 2020 with participants engaged in substance use treatment asking about the impact of the pandemic on social networks, substance use, access to treatment, and mental health. Interviews were coded and analyzed using grounded theory. Results Results centered around two main themes: (1) access to support (eg, formal and informal networks) and (2) individual outcomes regarding substance use and worsened mental health. Conclusions This research suggests that the COVID-19 pandemic has greatly disrupted access to resources for individuals in treatment for substance use, and calls for treatment centers and governing bodies to put more resources into telehealth and alternative treatment plans in the event of major disruptions, such as national disasters and global pandemics. Conflicts of Interest None declared.
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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) | 0.001 | 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".