‘Communities are attempting to tackle the crisis’: a scoping review on community plans to prevent and reduce opioid-related harms
Bibliographic record
Abstract
OBJECTIVES: We sought to understand the implementation of multifaceted community plans to address opioid-related harms. DESIGN: Our scoping review examined the extent of the literature on community plans to prevent and reduce opioid-related harms, characterise the key components, and identify gaps. DATA SOURCES: We searched MEDLINE, Embase, PsycINFO, CINHAL, SocINDEX and Academic Search Primer, and three search engines for English language peer-reviewed and grey literature from the past 10 years. ELIGIBILITY CRITERIA: Eligible records addressed opioid-related harms or overdose, used two or more intervention approaches (eg, prevention, treatment, harm reduction, enforcement and justice), involved two or more partners and occurred in an Organisation for Economic Co-operation and Development country. DATA EXTRACTION AND SYNTHESIS: Qualitative thematic and quantitative analysis was conducted on the charted data. Stakeholders were engaged through fourteen interviews, three focus groups and one workshop. RESULTS: We identified 108 records that described 100 community plans in Canada and the USA; four had been evaluated. Most plans were provincially or state funded, led by public health and involved an average of seven partners. Commonly, plans used individual training to implement interventions. Actions focused on treatment and harm reduction, largely to increase access to addiction services and naloxone. Among specific groups, people in conflict with the law were addressed most frequently. Community plans typically engaged the public through in-person forums. Stakeholders identified three key implications to our findings: addressing equity and stigma-related barriers towards people with lived experience of substance use; improving data collection to facilitate evaluation; and enhancing community partnerships by involving people with lived experience of substance use. CONCLUSION: Current understanding of the implementation and context of community opioid-related plans demonstrates a need for evaluation to advance the evidence base. Partnership with people who have lived experience of substance use is underdeveloped and may strengthen responsive public health decision making.
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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 | 0.046 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".