A comparison study on integrating electronic health records into priority simulation in undergraduate nursing education
Bibliographic record
Abstract
Normal 0 false false false EN-US X-NONE X-NONE Background: Electronic Health Records (EHR) make real time point of care efficient. We implemented EHR into our existing simulation exercise and attempted to evaluate the students’ perception on the effectiveness of the simulation in our undergraduate nursing students in comparison to the same cohort who had the same simulation without using electronic health records in the prior year. Purpose: The main purpose of this study is to assess the difference on simulation effectiveness perceived by the students in the group with and without utilizing EHR in simulation exercise. Method: A descriptive research design and convenience sampling was used to compare the effectiveness of perception data collected from the students after the simulation in these two groups. The difference in perception was compared by using the t -test. Result: There is no statistically significant change in students’ perception ( t = .79, p = .42) between the simulation (SIM) and the simulation with EHR integration (SIMEHR) group. The confidence in providing care and knowing the patient by utilizing electronic health records has been reported as between somewhat agree to strongly agree. Discussion: Integrating EHR into simulation did not significantly change students’ perception on simulation effectiveness. The implication from this study is that the integration of EHR into the simulation can be accomplished with careful prior planning with emphasis on introducing the strategies that enhance students’ ability to get familiarized with the EHR system.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".