A scoping review of opioid harm reduction interventions for equity-deserving populations
Bibliographic record
Abstract
Background: Morbidity and mortality associated with opioid use has become a North American crisis. Harm reduction is an evidence-based approach to substance use. Targeted harm reduction strategies that consider the needs of specific populations are required. The objective of this scoping review was to document the range of opioid harm reduction interventions across equity-deserving populations including racialized groups, Indigenous peoples, LGBTQIA2S+, people with disabilities, and women. Methods: Ten databases were searched from inception to July 5th, 2021. Terms for harm reduction and opioid use formed the central concepts of the search. We included studies that: (1) assessed the development, implementation, and/or evaluation of harm reduction interventions for opioid use, and (2) reported health-related outcomes or presented perspectives that directly related to experiences receiving or administering harm reduction interventions, (3) were completed within an equity-deserving population and (4) were completed in New Zealand, Australia, Canada or the US. A knowledge map was developed a-priori based on literature outlining different types of harm reduction interventions and supplemented by the expertise of the research team. Findings: = 11, 73%). The remaining four studies included: overdose prevention; drug testing equipment; and outreach, peer support, and educational programs for safer use. Nine studies focused on women, primarily pregnant/post-partum women, three focused on Indigenous peoples, and three studies included racialized groups. No studies were identified that provided any information on persons with a disability or members of the LGBTQIA2S+ population. Interpretation: The scant opioid specific harm reduction literature on equity-deserving populations to date has primarily focused on OAT programs and is focused primarily on women. There is a need for more targeted research to address the diverse social experiences of people who use drugs and the spectrum of harm reduction interventions that are needed. There is also a need to acknowledge the history of harm reduction as a drug-user activist movement aimed at challenging bio-medical paradigms of drug use. Further, there is a need to recognize that academic research may be contributing to health inequity by not prioritizing research with this lens. Funding: This research was funded by the Canadian Institutes of Health Research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".