Electronic Medical Record in the Simulation Hospital
Bibliographic record
Abstract
Nursing care delivery has shifted in response to the introduction of electronic health records. Adequate education using computerized documentation heavily influences a nurse's ability to navigate and utilize electronic medical records. The risk for treatment error increases when a bedside nurse lacks the correct knowledge and skills regarding electronic medical record documentation. Prelicensure nursing education should introduce electronic medical record documentation and provide a method for feedback from instructors to ensure proper understanding and use of this technology. RN preceptors evaluated two groups of associate degree nursing students to determine if introduction of electronic medical record in the simulation hospital increased accuracy in documenting vital signs, intake, and output in the actual clinical setting. During simulation, the first group of students documented using traditional paper and pen; the second group used an academic electronic medical record. Preceptors evaluated each group during their clinical rotations at two local inpatient facilities. RN preceptors provided information by responding to a 10-question Likert scale survey regarding the use of student electronic medical record documentation during the 120-hour inpatient preceptor rotation. The implementation of the electronic medical record into the simulation hospital, although a complex undertaking, provided students a safe and supportive environment in which to practice using technology and receive feedback from faculty regarding accurate documentation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".