Understanding synthetic drug analogues among the homeless population from the perspectives of the public: thematic analysis of Twitter data
Bibliographic record
Abstract
Objectives The last few years have seen the rapid emergence of synthetic drug derivatives known as new psychoactive substances (NPS) among the homeless population. Previous research has focused on understanding the issues from homeless or stakeholder perspectives but not the public’s. The purpose of this research is to understand the perspectives of the public and service providers regarding NPS use among the homeless population using thematic analysis of Twitter data.Method Tweets from Twitter were extracted and imported into NVivo 12 for thematic analysis. Tweets were included if they were written and related to NPS among the homeless. Excluded tweets were those related to interventional studies or with personalized information.Results The findings showed that two NPS were discussed on Twitter being novel synthetic opioids (NSOs) and synthetic cannabinoid receptor agonists (SCRAs). Thematic analysis of Twitter discussions revealed that individuals held a negative attitude toward the government and the services provided to the homeless NPS users, for both NSOs and SCRAs.Conclusion In summary, NSOs were more frequently discussed on the Twitter platform than SCRAs. NSOs were consumed by homeless population in the United States and Canada, and SCRA by the homeless in the United Kingdom.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".