Everywhere and for everyone: proportionate universalism as a framework for equitable access to community drug checking
Bibliographic record
Abstract
BACKGROUND: Illicit drug overdoses have reached unprecedented levels, exacerbated by the COVID-19 pandemic. Responses are needed that address the increasingly potent and unpredictable drug supply with better reach to a wide population at risk for overdose. Drug checking is a potential response offered mainly within existing harm reduction services, but strategies are needed to increase reach and improve equitable delivery of drug checking services. METHODS: The purpose of this qualitative study was to explore how to extend the reach of drug checking services to a wide population at risk of overdose. We conducted 26 in-depth interviews with potential service users to identify barriers to service use and strategies to increase equitable delivery of drug checking services. Our analysis was informed by theoretical perspectives on equity, and themes were developed relevant to equitable delivery through attention to quality dimensions of service use: accessibility, appropriateness, effectiveness, safety, and respect. RESULTS: Barriers to equitable service delivery included criminalization and stigma, geographic and access issues, and lack of cultural appropriateness that deter service use for a broad population with diverse needs. Strategies to enhance equitable access include 1ocating services widely throughout communities, integrating drug checking within existing health care services, reframing away from risk messaging, engaging peers from a broad range of backgrounds, and using discrete methods of delivery to help create safer spaces and better reach diverse populations at risk for overdose. CONCLUSIONS: We propose proportionate universalism in drug checking as a guiding framework for the implementation of community drug checking as an equity-oriented harm reduction intervention and as a population health response. Both a universal equity-oriented approach and multiple tailored approaches are required to facilitate drug checking services that maximize reach and appropriateness to respond to diverse needs.
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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 | 0.077 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.085 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.005 | 0.008 |
| 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".