Implementing a Comprehensive Unit-Based Safety Program (CUSP) to Enhance a Culture of Patient Safety and Improve Medication Safety in a Regional Home Care Program
Bibliographic record
Abstract
OBJECTIVE: To determine whether a Comprehensive Unit-based Safety Program could be used to enhance a culture of patient safety and improve medication safety at 1 pilot site. METHODS: The Canadian Patient Safety Culture Survey tool was used to assess the culture of patient safety and drill down on the key factors contributing to medication errors. Based on staff input and site improvement team investigations, solutions were developed to address medication safety issues. The main outcome measure was pre-/postintervention Canadian Patient Safety Culture scores. Change in frequency of occurrence reporting and staff's overall project experience were also measured. RESULTS: Overall perceptions of patient safety culture improved from 70% preintervention to 76.8% postintervention, representing a 9.7% change. Volume of occurrence reporting showed a marked increase postintervention. The project was well received by staff, with 84% rating their experience as "Good" to "Excellent." Finally 100% of participants reported that they learned something new and that this information could be applied to improve their day-to-day work. CONCLUSION: Implementing a Comprehensive Unit-based Safety Program improved staff's perceptions of patient safety, contributing to improved medication safety. To our knowledge, this is the first application of Comprehensive Unit-based Safety Program to successfully enhance patient safety in the home care setting.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".