Fentanyl test strip use and homelessness among people who use drugs in Rhode Island
Bibliographic record
Abstract
BACKGROUND: People experiencing homelessness have been disproportionately affected by the overdose crisis in the United States. We assessed whether an association exists between homelessness and fentanyl test strip (FTS) use to avoid drug overdose among people who use drugs in Rhode Island. METHODS: We recruited 505 participants aged 18-65 from September 2020 to February 2023 as part of the Rhode Island Prescription Illicit Drug Study (RAPIDS). Baseline data assessed correlates of past-month FTS use through bivariate and multivariable analyses. We used generalized estimating equations (GEE) to estimate the longitudinal association between past-month homelessness and FTS use over 12 months. RESULTS: At baseline, 19.3 % of the sample reported having used FTS in the past month, which was more commonly reported by those who were experiencing homelessness (22.6 %) compared to those who were housed (14.4 %), p = 0.021. In bivariable GEE analysis, past month homelessness was not associated with past-month FTS use (OR=1.22, 95 %CI: 0.95--1.56, p = 0.117). In multivariable GEE analysis, homelessness was not associated with FTS use, but regular use of crystal methamphetamine was (aOR = 2.13, 95 %CI: 1.42-3.19; p < 0.001). The odds of FTS use among persons recruited in 2023 increased by 165 % (aOR=2.65, 95 %CI: 1.22-5.76; p < 0.001) compared to those recruited in 2020. CONCLUSIONS: We found that housing status was not independently and longitudinally associated with FTS use; however, people who used crystal methamphetamine were more than twice as likely to have used FTS in the past month. Future research exploring how differential housing situations may affect uptake of harm reduction services is needed to prevent fentanyl overdose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".