Perceptions of Emergency Department Nurses on Substance Use Disorders and Supervised Consumption Sites
Bibliographic record
Abstract
Background: There have been increased deaths, emergency medical services, emergency department (ED) visits, and hospitalizations due to substance misuse (Government of Canada, 2022; WECHU, 2021; WECHU, 2022b; 2022c). With increasing drug-related harms, a stronger emphasis has been placed on harm reduction strategies such as supervised consumption sites (SCSs) (Kerr et al., 2017).Purpose: People with substance use disorders are among those who make persistent, frequent ED visits in Ontario (Moe et al., 2022). An ED visit has been recognized as an opportunity to improve patient outcomes by identifying those with substance use disorders and connecting them to treatment (Hawk & Onofrio, 2018). This study aims to assess ED nurses perceptions of substance use disorders and SCSs in Southwestern Ontario.Literature Review: Databases searched include CINAHL, ProQuest, Ovid Medline, PubMed, and Google Scholar using the following keywords:safe injection site or facility, supervised sites, safe or supervised consumption sites, harm reduction, overdose prevention, people who use drugs, drug use,inject, overdose, overdose death, opioids, mortality, morbidity, substance use or abuse, substance use disorder, perceptions, opinions, views, attitudes, 'perspectives', emergency department or room, and nurs*. Gray literature was also searched. A total of 36 research studies and 22 grey literature sources were included.Methods: Quantitative approach and descriptive design were used. The survey tool was developed in Qualtrics and deployed electronically to RNs in 6 EDs across 4 Southwestern Ontario hospitals.Results: The final results of this study are pending as data collection is currently ongoing. Conclusion: Knowing the perceptions of ED nurses can help create and enforce harm reduction programs and strategies and help to understand the viewpoints of those providing direct patient care (Shreffler et al., 2021).
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".