Behavior change after fentanyl testing at a safe consumption space for women in Northern Mexico: A pilot study
Bibliographic record
Abstract
BACKGROUND: Fentanyl has led to an increased number of overdose deaths in North America. Testing substances for fentanyl may be a harm reduction strategy to prevent overdose. Little is known about behavior change after fentanyl testing and the attitudes around fentanyl knowledge and testing along the US-Mexico border in the context of a safe consumption site. METHODS: This was a pilot quantitative and qualitative study with 30 women who use drugs at an unsanctioned safe consumption site in Mexicali, Mexico. Women participated in a quantitative survey, a semi-structured interview, and fentanyl testing of substances. Injection behavior was observed after fentanyl testing results were provided. Qualitative data were collected to explore the meanings participants attributed to fentanyl and fentanyl testing. RESULTS: Half of the substances tested positive for fentanyl (n=15, 50%), and all of them were in samples of black tar heroin. Among those participants who tested positive for fentanyl, 7 (47%) subsequently used less of the intended substance, 1 did not use the intended substance, and 7 (47%) did not change their behavior (i.e., used as originally intended). In qualitative interviews, a predominant theme was a description of fentanyl as dangerous and deadly and fentanyl testing as being helpful for modifying drug use behaviors. However, participants recognized that there could be no change in behavior following a positive fentanyl test in the context of not being able to find substances free of fentanyl. CONCLUSION: We observed mixed results related to behavior change after women's intended substance for use tested positive for fentanyl. Fentanyl testing was acceptable to women, but behavior change was hampered by the inability to find substances free of fentanyl. Further research is needed to maximize the potential of fentanyl testing as a harm reduction tool especially in the context of a changing drug supply.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".