Informal Coping Strategies Among People Who Use Opioids During COVID-19: Thematic Analysis of Reddit Forums
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has transformed how people seeking to reduce opioid use access treatment services and navigate efforts to abstain from using opioids. Social distancing policies have drastically reduced access to many forms of social support, but they may have also upended some perceived barriers to reducing or abstaining from opioid use. OBJECTIVE: This qualitative study aims to identify informal coping strategies for reducing and abstaining from opioid use among Reddit users who have posted in opioid-related subreddits at the beginning of the COVID-19 pandemic. METHODS: We extracted data from 2 major opioid-related subreddits. Thematic data analysis was used to evaluate subreddit posts dated from March 5 to May 13, 2020, that referenced COVID-19 and opioid use, resulting in a final sample of 300 posts that were coded and analyzed. RESULTS: Of the 300 subreddit posts, 100 (33.3%) discussed at least 1 type of informal coping strategy. Those strategies included psychological and behavioral coping skills, adoption of healthy habits, and use of substances to manage withdrawal symptoms. In addition, 12 (4%) subreddit posts explicitly mentioned using social distancing as an opportunity for cessation of or reduction in opioid use. CONCLUSIONS: Reddit discussion forums have provided a community for people to share strategies for reducing opioid use and support others during the COVID-19 pandemic. Future research needs to assess the impact of COVID-19 on opioid use behaviors, especially during periods of limited treatment access and isolation, as these can inform future efforts in curbing the opioid epidemic and other substance-related harms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".