A nursing perspective on barriers to implementing harm reduction in acute care hospital settings: A scoping review
Bibliographic record
Abstract
Harm reduction strategies focusing on substance use have been largely implemented in communities. However, it has been underutilized in managing in-patient environments. When patients with substance use disorder (SUD) are hospitalized, without harm reduction management, they may engage in risky behaviours, leading to unsafe opioid use. Negative encounters with the healthcare system and discriminatory attitudes towards patients with SUD by healthcare professionals also contribute to health issues and safety concerns. This study reviewed existing literature on barriers to implementing harm reduction strategies in acute care hospitals. Three databases were searched for peer-reviewed articles published from 2014 to 2024. After screening 987 articles, ten met the inclusion criteria. The findings highlighted challenges nurses and patients encounter in implementing harm reduction in acute care hospitals, including stigma, safety concerns, educational gaps, and clinician burnout. Addressing these challenges entails nurse education and organizational changes. While the current research provides some insights, further studies should examine standardizing care plans for individuals with SUD, healthcare agencies' roles in promoting harm reduction education, and nurses' perspectives to enhance harm reduction strategies in in-patient settings. Keywords: nurses, substance use disorder, in-patient, harm reduction
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".