Web-Based Virtual Learning Environment for Medicine Administration in Pediatrics and Neonatology: Content Evaluation
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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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".