Using simulation to assess undergraduate nursing students’ competency with medication administration to a limited- English language proficiency patient
Bibliographic record
Abstract
Background/Objectives: Medication errors continue to be an area of concern in health care and are a significant cause of morbidity and mortality in both inpatient and outpatient settings. Little consideration has been given to the role of the patient in the medication process; patients with limited-English language proficiency (LEP) may be at risk for medical errors and drug complications. The purpose of this research study is to assess undergraduate nursing students’ competency with medication administration to a simulated patient with LEP. Methods: A non-experimental quantitative design using the Medication Administration Safety Assessment Tool (MASAT) (Copyright © 2013 Goodstone & Goodstone) for student competency in medication administration perfor- mance was conducted in a simulation environment. Qualitative data was also collected during debriefing for questions specific to safety, medication administration, and barriers related to care. Results: Student scores on the MASAT identified a less than expected probability of success. Students’ performance did not show proficiency in the six rights of medication administration; in addition, students’ were inattentive to the language needs of a LEP patient. Conclusion: Results from this study indicate further experiential learning activities may be necessary to reinforce safety in medication administration. Students were not attentive to the language needs of the simulated patient and may require additional learning opportunities through simulation to assist students in responsive behaviors that support LEP patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".