An Analysis of Priorities in Developing Virtual Reality Programs for Core Nursing Skills: Cross-sectional Descriptive Study Using the Borich Needs Assessment Model and Locus for Focus Model
Bibliographic record
Abstract
BACKGROUND: There are limitations to conducting face-to-face classes following the recent COVID-19 pandemic. Web-based education is no longer a temporary form of teaching and learning during unusual events, such as pandemics, but has proven to be necessary to uphold in parallel with offline education in the future. Therefore, it is necessary to scientifically organize the priorities of a learner needs analysis by systematically and rationally investigating and analyzing the needs of learners for the development of virtual reality (VR) programs for core nursing skills (CNS). OBJECTIVE: This study aimed to identify the priorities of learners' needs for the development of VR programs for CNS using the Locus for Focus Model and Borich need assessment model. METHODS: The participants included nursing students in South Korea who were in their second year or higher and had taken courses in fundamental nursing or CNS-related classes. The survey took place from May 20 to June 25, 2021. A total of 337 completed questionnaires were collected. Of these, 222 were used to conduct the final analysis. The self-report questionnaire consisted of 3 parts: perception of VR programs, demand for developing VR programs, and general characteristics. The general characteristics of the participants were analyzed using descriptive statistics. To determine the priority of the demand for developing VR programs for CNS, the Locus for Focus Model and the Borich priority formula were used. RESULTS: In all, 7 skills were identified as being of the top priority for development, including intramuscular injection, intradermal injection, tube feeding, enema, postoperative care, supplying oxygen via nasal cannula, and endotracheal suction. CONCLUSIONS: The analysis showed that nursing students generally needed and prioritized the development of VR programs for the nursing skills involving invasive procedures. The results of this study are intended to help in various practical education classes using VR programs in nursing departments, which are currently facing difficulties in teaching CNS on the web owing to COVID-19.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".