Threats to Narcotic Safety—A Narrative Review of Narcotic Incidents, Discrepancies and Near-Misses Within a Large Canadian Health System
Bibliographic record
Abstract
BACKGROUND: Canada is currently experiencing an opioid crisis. PURPOSE: Nurses are the largest number of frontline healthcare professionals in Canada who administer narcotic pharmacotherapy, hence, they are ideally placed to improve narcotic stewardship in hospitals. Our study aims to understand the characteristics of narcotic incidents and hence recommend interventions for narcotic stewardship. METHODS: Our study was conducted within a 442-bed academic health sciences center in Ontario. We extracted anonymized narcotic incident reports which occurred over a 3-year period from the SAFER System. Descriptive statistics were utilized to analyze narcotic incidents and their contributory factors. RESULTS: 272 narcotic incident reports were submitted to SAFER within the study period. Most incidents (51%) involved hydromorphone and morphine and were primarily categorized as Level I (n = 154) and Level II (n = 60). Incorrect narcotic dosing (44%), and narcotic count discrepancies (27%) were most commonly reported with active failures being the most commonly reported contributory factors such as failure to review medication orders prior to narcotic administration. CONCLUSIONS: Nurses have an important role in narcotic safety as an intermediary between narcotic administration and incident reporting. Further research is needed to understand the enablers, barriers and opportunities for nurses and other healthcare professionals to improve narcotic stewardship.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".