Electronic Medical Record in the Simulation Hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it