Emergency Department Nurses Attitudes Toward Barcode Medication Administration
Bibliographic record
Abstract
Abstract Background: Barcode medication administration (BCMA) has been widely implemented in the inpatient setting of hospitals throughout the United States, resulting in lower medication administration errors. Understanding nurses’ attitudes toward BCMA in the Emergency Department (ED) may assist administrators with creating implementation strategies that will improve medication administration process turnaround time and remove barriers to use ensuring increased compliance and improved patient safety. Methods: The aim of this descriptive research study was to identify Emergency Department nurses’ attitudes towards acceptance of this technology, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Data collection was carried out using an online, cross-sectional survey of nurses (n=55) who were members of the National Emergency Nurses Association of Canada. Results: The results demonstrated that two-thirds of those surveyed had approximately one year of experience with using BCMA technology. More positive attitudes were found in the following domains: behavioral intent, anxiety, and self-efficacy. Neutral attitudes were perceived regarding facilitating conditions, social influence, and effort expectancy. The most negative attitudes were expressed regarding attitude toward technology and performance expectancy. Conclusions: The results of this study allow us to conclude that the ED nurse perceived BCMA as easy to master and use and not intimidating or anxiety producing; however, they do not perceive it as useful nor do they perceive it to improve their proficiency or productivity. It is recommended that future studies be conducted on larger samples and also on participants that have had more experience using this technology. Keywords: Barcode Medication Administration, Emergency Department, Medication Administration, Attitudes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".