The effect of early adoption of an academic electronic health record system in nursing education: A pilot outcome study
Bibliographic record
Abstract
Objective: This study aimed to explore the faculty’s and students’ perceptions of an academic electronic health record system (AEHRs) for teaching/learning electronic nursing documentation and to assess the outcomes of the AEHRs on nursing students’ competency with electronic nursing documentation.Methods: With a mixed-method pilot study, a convenience sample of 41 undergraduate nursing students and a purposive sample of 7 faculty and 9 students were used. Two groups of student participants for the quantitative data were compared for their competency with electronic nursing documentation. For the qualitative data, an in-depth, exploratory approach to data collection was taken for the nursing faculty and the intervention group.Results: For the quantitative findings, the early adoption of an AEHRs could help students to collect a patients’ health information through the system even though it may not impact their critical thinking on a patient’s care. For the qualitative findings, three key themes were shared by the faculty and students: (1) benefits and challenges, (2) impact of the AEHRs, and (3) recommendations for future adoption.Conclusions: This study revealed that the successful adoption of an AEHRs includes many steps that can be used to create positive improvements. These findings were beneficial to prepare students and nursing educators for the future of health information technology. Meaningful adoption of an AEHRs will help in building the competence of undergraduate nursing students in electronic nursing documentation and improve patient care.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".