Uptake into a bedside needle and syringe program for acute care inpatients who inject drugs
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Injection drug use is associated with significant morbidity and mortality worldwide. Needle and syringe programs (NSP) have been shown to reduce negative health outcomes for people who inject drugs. However, NSPs have limited reach in hospitals, and no peer-reviewed research has examined NSP implementation in acute care settings. We describe the implementation of an inpatient NSP offered through an addiction medicine consultation service in a large, urban acute care hospital in Edmonton, Canada, and compared characteristics of inpatients who did versus did not access the NSP. DESIGN AND METHODS: Administrative data were reviewed for all addiction medicine consult service intakes between 11 July 2016 and 14 January 2018. We calculated the proportion of intakes in which patients: (i) were offered syringes; and (ii) accepted syringes. Multivariate analyses were used to examine associations between these outcomes and patient age and sex. RESULTS: Patients reported injecting drugs in 597 (31%) of 1907 intakes during the study period. People who inject drugs were offered syringes in 334 (56%) of these intakes, and accepted syringes in 124 (37%) of them. Female patients were more likely to accept syringes. DISCUSSION AND CONCLUSIONS: In a recently implemented NSP for hospital inpatients, just over half of patients who reported injection drug use were offered syringes, and the rate of patient acceptance was low. Further research is necessary to describe best practice for inpatient NSPs and identify and remove any barriers that prevent some inpatients from either being offered or accepting syringes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".