Naloxone protection, social support, network characteristics, and overdose experiences among a cohort of people who use illicit opioids in New York City
Bibliographic record
Abstract
BACKGROUND: Despite increased availability of take-home naloxone, many people who use opioids do so in unprotected contexts, with no other person who might administer naloxone present, increasing the likelihood that an overdose will result in death. Thus, there is a social nature to being "protected" from overdose mortality, which highlights the importance of identifying background factors that promote access to protective social networks among people who use opioids. METHODS: We used respondent-driven sampling to recruit adults residing in New York City who reported recent (past 3-day) nonmedical opioid use (n = 575). Participants completed a baseline assessment that included past 30-day measures of substance use, overdose experiences, and number of "protected" opioid use events, defined as involving naloxone and the presence of another person who could administer it, as well as measures of network characteristics and social support. We used modified Poisson regression with robust variance to estimate unadjusted and adjusted prevalence ratios (PRs) and 95% confidence intervals (CIs). RESULTS: 66% of participants had ever been trained to administer naloxone, 18% had used it in the past three months, and 32% had experienced a recent overdose (past 30 days). During recent opioid use events, 64% reported never having naloxone and a person to administer present. This was more common among those: aged ≥ 50 years (PR: 1.18 (CI 1.03, 1.34); who identified as non-Hispanic Black (PR: 1.27 (CI 1.05, 1.53); experienced higher levels of stigma consciousness (PR: 1.13 (CI 1.00, 1.28); and with small social networks (< 5 persons) (APR: 1.14 (CI 0.98, 1.31). Having a recent overdose experience was associated with severe opioid use disorder (PR: 2.45 (CI 1.49, 4.04), suicidality (PR: 1.72 (CI 1.19, 2.49), depression (PR: 1.54 (CI 1.20, 1.98) and positive urinalysis result for benzodiazepines (PR: 1.56 (CI 1.23, 1.96), but not with network size. CONCLUSIONS: Results show considerable gaps in naloxone protection among people who use opioids, with more vulnerable and historically disadvantaged subpopulations less likely to be protected. Larger social networks of people who use opioids may be an important resource to curtail overdose mortality, but more effort is needed to harness the protective aspects of social networks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".