Web-Based Nursing Care Documentation for Students to Support Online Learning
Bibliographic record
Abstract
Nursing care is the most critical element in nursing services, which aims to improve the patient’s health status. Ineffective and inefficient care documentation can impact the quality of nursing services. However, the increasingly advanced development of technology provides freedom for the health world to improve the quality of patient-centered services. Educational institutions have also used information technology in learning process activities. As prospective health professionals, students must be equipped with competencies to support their performance. In this study, a web-based nursing care information system was developed to assist students in documenting care activities. The website application was designed to increase student competency in nursing documentation activities to provide high-quality nursing services even though learning is online. The waterfall model approach was used in application design. The design stage started with analyzing application requirements, followed by system design and coding. Next, using a Likert-scale questionnaire, a usability test was performed on 15 beta users to assess the functionality of the application. The results showed that the nursing care website application was easy to use and that students felt satisfied. It is hoped that the website application can be made more attractive by including a decisional support system to make it easier for students to enforce nursing diagnoses on patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".