Emergency Department Registered Nurses’ Perceptions of Substance Use Disorders and Supervised Consumption Sites
Bibliographic record
Abstract
Abstract Background: Canada is facing increased drug-related harms; thus, a stronger emphasis has been placed on harm reduction strategies such as supervised consumption sites (SCSs). There is a lack of literature on emergency department (ED) registered nurses' (RNs) perceptions of SCSs and substance use disorders (SUDs), especially in small to mid-sized Canadian cities. Purpose: This study aimed to determine ED RNs’ perceptions of SUDs and SCSs. Methods: A 27-question survey was sent to RNs currently working in EDs in Southwestern Ontario using an online Qualtrics® link. The research explored ED RNs’ perceptions of SCSs and SUDs. Results: Quantitative results indicated that ED RNs (n = 146) were understanding of drug use and SUDs but felt neutral towards SCSs. They indicated positive impacts and potential concerns of SCS implementation. However, most ED RNs reported that they would still refer their patients to such sites if one was available, despite their apprehensions. Conclusion: This research demonstrates the importance of harm reduction education in nursing curricula and the workplace. Recommendations include a harm reduction referral partnership between the ED and community partners. It is essential to practice reflectively, decrease the influence of stereotypes and stigma-based decisions and care, and encourage legislation that supports ethical policies and procedures that increase the use and access to SCSs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".