An Exploration of Nursing Medication Practices in Acute Care Settings in Canada
Bibliographic record
Abstract
Background. Nurses must balance heavy workloads with a diverse array of practice standards, principles, current evidence, and medication rights, yet it is unknown how these complex and often confusing requirements are enacted by nurses during the medication workflow in the clinical setting. Instead, medication administration has been described as a complex, multi-step process that is not well understood. Purpose. The purpose of this study was to explore the practices Canadian registered nurses use most frequently during the complex workflow of medication administration in acute care settings and the factors that influence these practices. Methods. An exploratory quantitative design, including a comprehensive review of the literature and a web-based survey, was used to answer the following research questions: 1. What medication workflow practices do registered nurses use most often when administering medications in acute care hospitals in Canada? 2. How do participants rate the influence of the ten factors that are thought to influence medication administration practices? 3. Does practice setting influence medication workflow practices? 4. Does registered nurse years of experience influence medication workflow practices? Results. Five medication workflow phases emerged from the literature, providing concept clarity with identifiable boundaries. The workflow phases included order practices, assessment practices, preparation practices, administration practices, and post administration practices. Study findings not only shed light on medication workflow practices used most often by participants in this study, but also on medication workflow practices used infrequently or not at all. The most frequently performed medication workflow practices used by nurses included reviewing lab values, checking chart for allergies, assessing IV catheter sites, administering pre- and post-IV bolus flushes, assessing appropriateness of new orders, and positioning patients for safety. Practices performed least often included asking the patient to state name and birthdate as a form of patient identification; checking for drug-diet and drug-drug interactions; assessing patients’ medication related values, beliefs and preferences; explaining medication purpose and side-effects; and engaging patients in educational discussions about their medications. Differences in medication practices were found when looking at practice setting and RN experience. The top-rated factors influencing medication practices included environment, followed by medication factors, and teamwork factors. Leadership was the lowest rated influencing factor on medication practices in this study. Conclusion. Medication administration is complex and highly regulated, yet there is no consensus regarding the language used to describe the workflow, when the workflow begins or ends, or which elements of the workflow are considered most important. Studies involving medication workflow incorporated a diverse range of pre-defined criteria and process steps that were developed by experts and that measured nurse compliance. No studies could be found that asked nurses what practices they use when enacting the medication workflow. This study provides baseline data that can be used to further explore the challenges faced by nurses when translating theory to practice in the clinical setting. Technology solutions are needed to improve access to information and to help reduce nurse workload. Clearly defined roles are needed for all professionals that play a role in the medication workflow. Finally, the development of clear, evidence-based medication workflow priorities and practices, in partnership with frontline nurses, is critical to improving medication safety in hospitals
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".