Identifying harm reduction strategies for alcohol and drug-use in inpatient care settings and emergency departments: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: People who use alcohol and/or drugs (PWUAD) are at high risk of medical complications, frequent hospitalisation and drug-related death following discharge from inpatient settings and emergency departments (EDs). Harm reduction strategies implemented in these settings may mitigate negative health outcomes for PWUAD. However, the scope of harm reduction strategies used globally within inpatient settings and EDs is unknown. The objective of this review is to identify and synthesise reported harm reduction strategies that have been implemented across inpatient settings and EDs for PWUAD. METHODS AND ANALYSIS: This review will include studies from any country and health service reporting on harm reduction strategies implemented in inpatient settings or EDs. The population of interest includes people of any race, gender and age identifying as PWUAD, or individuals who provided care to PWUAD. Studies which describe implementation strategies and barriers and enablers to implementation will be included. Studies published in English, or those available for English translation will be included. The following databases will be searched: MEDLINE All (Ovid), Embase (Elsevier Embase.com), CINAHL with Full Text (EBSCOhost), PsycINFO (EBSCOhost) and SCOPUS (Elsevier Scopus.com). A grey literature search will be conducted. There will be no date restrictions on the search. Titles, abstracts and full texts will be screened in duplicate. Data will be extracted using a standardised form. The results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews. ETHICS AND DISSEMINATION: Scoping reviews do not require ethical approval. Patient partners with lived experience and relevant knowledge users will be engaged as research team members throughout all phases of the research process. A report detailing context, methodology and findings from this review will be disseminated to knowledge users and relevant community stakeholders. This review will be submitted for publication to a relevant peer-reviewed journal.
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 | 0.086 | 0.067 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.014 |
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".