A rapid review of current engagement strategies with people who use drugs in monitoring and reporting on substance use-related harms
Bibliographic record
Abstract
BACKGROUND: The Canadian drug supply has significantly increased in toxicity over the past few years, resulting in the worsening of the overdose crisis. A key initiative implemented during this crisis has been data monitoring and reporting of substance use-related harms (SRH). This literature review aims to: (1) identify strategies used for the meaningful engagement of people who use drugs (PWUD) in local, provincial, and national SRH data system planning, reporting, and action and (2) describe data monitoring and reporting strategies and common indicators of SRH within those systems. METHODS: We searched three academic and five gray literature databases for relevant literature published between 2012 and 2022. Team members who identify as PWUD and a librarian at Public Health Ontario developed search strings collaboratively. Two reviewers screened all search results and applied the eligibility criteria. We used Microsoft Excel for data management. RESULTS: Twenty-two articles met our eligibility criteria (peer-reviewed n = 10 and gray literature reports n = 12); most used qualitative methods and focused on the Canadian context (n = 20). There were few examples of PWUD engaged as authors of reports on SRH monitoring. Among information systems involving PWUD, we found two main strategies: (1) community-based strategies (e.g., word of mouth, through drug sellers, and through satellite workers) and (2) public health-based data monitoring and communication strategies (e.g., communicating drug quality and alerts to PWUD). Substance use-related mortality, hospitalizations, and emergency department visits were the indicators most commonly used in systems of SRH reporting that engaged PWUD. CONCLUSION: This review demonstrates limited engagement of PWUD and silos of activity in existing SRH data monitoring and reporting strategies. Future work is needed to better engage PWUD in these processes in an equitable manner. Building SRH monitoring systems in partnership with PWUD may increase the potential impact of these systems to reduce harms in the community.
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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 | 0.037 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.038 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".