Correlates of fentanyl preference among people who use drugs in Rhode Island
Bibliographic record
Abstract
BACKGROUND: Fentanyl is increasingly pervasive in the unregulated drug supply and is a driver of drug overdose deaths in the United States. The aims of this study were to characterize and identify correlates of fentanyl preference among people who use drugs (PWUD) in Rhode Island (RI). METHODS: Using bivariate analysis, we examined associations between fentanyl preference and sociodemographic and psychosocial characteristics at baseline among participants enrolled in the RI Prescription Drug and Illicit Drug Study from August 2020-February 2023. Fentanyl preference was operationalized based on responses to a five-point Likert scale: "I prefer using fentanyl or drugs that have fentanyl in them." Participants who responded that they "strongly disagree," "disagree," or were "neutral" with respect to this statement were classified as not preferring fentanyl, whereas participants who responded that they "agree" or "strongly agree" were classified as preferring fentanyl. RESULTS: Among 506 PWUD eligible for inclusion in this analysis, 15% expressed a preference for fentanyl or drugs containing fentanyl as their drug of choice. In bivariate analyses, preference for fentanyl was positively associated with younger age, white race, lifetime history of overdose, history of injection drug use, past month enrollment in a substance use treatment program, past month treatment with medications for opioid use disorder, and preferences for heroin and crystal methamphetamine (all p < 0.05). Descriptive data yielded further insight into reasons for fentanyl preference, the predominant having to do with perceived effects of the drug and desire to avoid withdrawal symptoms. CONCLUSIONS: Only a relatively small subset of study participants preferred drugs containing fentanyl. Given the increased prevalence of fentanyl contamination across substances within the unregulated drug market, the result for PWUD is increasingly less agency with respect to choice of drug; for example, people may be forced to use fentanyl due to restricted supply and the need to mitigate withdrawal symptoms, or may be using fentanyl without intending to do so. Novel and more effective interventions for PWUD, including increased access to age-appropriate harm reduction programs such as fentanyl test strips and overdose prevention centers, are needed to mitigate fentanyl-related harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".