Harm reduction strategies in acute care for people who use alcohol and/or drugs: A scoping review
Bibliographic record
Abstract
BACKGROUND: People who use alcohol and/or drugs (PWUAD) are at higher risk of infectious disease, experiencing stigma, and recurrent hospitalization. Further, they have a higher likelihood of death once hospitalized when compared to people who do not use drugs and/or alcohol. The use of harm reduction strategies within acute care settings has shown promise in alleviating some of the harms experienced by PWUAD. This review aimed to identify and synthesize evidence related to the implementation of harm reduction strategies in acute care settings. METHODS: A scoping review investigating harm reduction strategies implemented in acute care settings for PWUAD was conducted. A search strategy developed by a JBI-trained specialist was used to search five databases (Medline, Embase, CINAHL, PsychInfo and Scopus). Screening of titles, abstracts and full texts, and data extraction was done in duplicate by two independent reviewers. Discrepancies were resolved by consensus or with a third reviewer. Results were reported narratively and in tables. Both patients and healthcare decision makers contributing to the development of the protocol, article screening, synthesis and feedback of results, and the identification of gaps in the literature. FINDINGS: The database search identified 14,580 titles, with 59 studies included in this review. A variety of intervention modalities including pharmacological, decision support, safer consumption, early overdose detection and turning a blind eye were identified. Reported outcome measures related to safer use, managed use, and conditions of use. Reported barriers and enablers to implementation related to system and organizational factors, patient-provider communication, and patient and provider perspectives. CONCLUSION: This review outlines the types of alcohol and/or drug harm reduction strategies, which have been evaluated and/or implemented in acute care settings, the type of outcome measures used in these evaluations and summarizes key barriers and enablers to implementation. This review has the potential to serve as a resource for future harm reduction evaluation and implementation efforts in the context of acute care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".