Web-Based Virtual Learning Environment for Medicine Administration in Pediatrics and Neonatology: Content Evaluation
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
BACKGROUND: Worldwide, patient safety has been a widely discussed topic and has currently become one of the greatest challenges for health institutions. This concern is heightened when referring to children. OBJECTIVE: The goal of this study was to develop a virtual learning environment for medication administration, as a tool to facilitate the training process of undergraduate nursing students. METHODS: Descriptive research and methodological development with a quantitative and qualitative approach were used with stages of design-based research as methodological strategies. For the development of the virtual environment, 5 themes were selected: rights of medication administration, medication administration steps, medication administration routes, medication calculation, and nonpharmacological actions for pain relief. After development, 2 groups-expert judges in the field of pediatrics and neonatology for environment validation and undergraduate nursing students for the assessment-were used to assess the virtual learning environment. For the validation of the virtual learning environment by expert judges, the content validity index was used, and for the evaluation of the students, the percentage of agreement was calculated. RESULTS: The study included 13 experts who positively validated the virtual environment with a content validity index of 0.97, and 26 students who considered the content suitable for nursing students, although some adjustments are necessary. CONCLUSIONS: The results show the benefit of the virtual learning environment to the training of nursing students and professional nurses who work in health care. It is an effective educational tool for teaching medication administration in pediatrics and neonatology and converges with the conjectures of active methodologies.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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