Nurses' Perceptions of Medication Safety and Medication Reconciliation Practices
Bibliographic record
Abstract
Medication reconciliation (MR) involves the accurate transfer of medication information across the continuum of care. The aim of this study was to measure nurses perceptions of patient safety, medication safety and current MR practice at transition points in a patient's hospital stay. Surveys were distributed to 111 nursing staff in three general medicine units at Capital Health District, Nova Scotia, in August 2005. A total of 39 nurses (35% response rate) completed the survey. "Teamwork within units" was the safety culture dimension with the highest positive response (98.1%), while the processes of handoffs and transitions received the lowest positive response (42.8%). Key areas identified for improvement relative to the current level of MR practice include institutional patient safety systems (e.g., low confidence in existing systems and procedures), inconsistent practices (e.g., wide variation in whether community pharmacists are contacted to verify medication profiles), lack of communication (e.g., between healthcare professionals) and staffing resources (e.g., MR is perceived as a very time-consuming process). Addressing these challenges prior to implementing a formalized MR program should help to ensure success of the project. The insights gained through the use of this survey may prove valuable to other Canadian healthcare organizations that are implementing MR services.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".