Electronic harm reduction interventions for drug overdose monitoring and prevention: A scoping review
Bibliographic record
Abstract
BACKGROUND: Novel strategies are required to address rising overdose deaths across the globe. We sought to identify the breadth and depth of the existing evidence around electronic harm reduction (e-harm reduction) interventions that aimed to reduce the harms associated with substance use. METHODS: We conducted a scoping review according to the PRISMA-ScR and PRISMA for Searching guidelines. A health sciences librarian systematically searched seven health databases from inception until January 20, 2023. Citation chaining and reference lists of included studies were searched to identify additional articles. Two reviewers independently screened, extracted and charted the data. Additionally, we conducted a gray literature search and environmental scan to supplement the findings. RESULTS: A total of 51 studies met the criteria for inclusion (30 peer-reviewed articles and 21 non-peer reviewed). Most peer-reviewed studies were conducted in Western countries (USA = 23, Canada = 3, Europe = 3, China = 1) and among adult samples (adult = 27, youth/adults =1, unspecified = 2). Study designs were predominantly quantitative (n = 24), with a minority using qualitative (n = 4) or mixed methods (n = 2). Most e-harm reduction interventions were harm reduction (n = 15), followed by education (n = 6), treatment (n = 2), and combined/other approaches (n = 7). Interventions utilized web-based/mobile applications (n = 15), telephone/telehealth (n = 10), and other technology (n = 5). CONCLUSIONS: While e-harm reduction technology is promising, further research is required to establish the efficacy and effectiveness of these novel interventions.
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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 | 0.044 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".