International Consensus Statement on the Role of Nurses in Supervised Consumption Sites
Bibliographic record
Abstract
Background and Objective There are currently more than 150 supervised consumption sites (SCS) worldwide. These sites offer a much-needed point of contact between the health care system and people who use drugs and, as such, have been proven to effectively reduce harms and improve health. SCS are typically staffed by mental health and harm reduction workers, social workers, workers with living or lived experience, and registered nurses. It has been established that the care provided by nurses within SCS fall within their legislated scope of practice but the actual role of nurses in SCS remains poorly defined and understood. Material and Methods To address this significant practice, policy and research gap, a consensus statement was developed based on information generated by 17 content experts from 10 countries namely, Canada, Spain, Australia, France, Denmark, Norway, Ireland, Switzerland, Germany, and Scotland. The statement was developed from “the ground up” by gathering information on three content areas: nursing practice in SCS, training, and needs. This information was summarized, and then submitted to two rounds of voting using a modified Delphi method to build consensus. Results The final content of the consensus statement is comprised of five sections: (1) Philosophy of care, (2) Framework, (3) Nursing role, (4) Training requirements, and (5) Needs of nurses. Conclusion This consensus statement is the first step toward a better understanding of the role of nurses in SCS. There is immense responsibility on nurses in this setting, as the majority of people who access SCS face many barriers in accessing other health and social services, even when their need for those services may be critical. For these reasons, it is essential to better prepare nurses for these realities. We hope that this first international consensus statement can serve as a foundation to guide practice, policy, research, and operational decisions in SCS.
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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 | 0.207 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".