Management of illicit substances in hospital: An evaluation of policy and procedure implementation
Bibliographic record
Abstract
Previous research has identified illicit substance use on hospital property as an ongoing concern, particularly in inpatient mental health units. This research, combined with concerns raised by healthcare providers, patients, and patients' families, resulted in one hospital in a medium-sized city in Canada enacting two internal strategies for the management of illicit substances on hospital property. The unit-based Green-Yellow-Red procedure employs environmental scanning and regular risk assessment to report the incidence rate of illicit substances suspected and/or found in the unit, to inform staff of the extent of necessary interventions which should ensue. The hospital-wide Management of Illicit Substances protocol includes ten steps which can be followed by any staff member who suspects they have found an illicit substance or related paraphernalia on hospital grounds. This paper discusses the creation and implementation of these two strategies, as well as associated challenges and outcomes of each. Overall, these strategies have effectively functioned to mitigate the potential dangers of exposure to illicit substances for staff and patients alike. These results stand to encourage other institutions to implement similar strategies in order to better manage situations in which illicit substances are suspected or discovered on hospital property.
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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 | 0.188 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".