Exploring Key Factors Influencing Nursing Students’ Cognitive Load and Willingness to Serve Older Adults: Cross-sectional Descriptive Correlational Study
Bibliographic record
Abstract
Background Virtual learning environments (VLEs) use a virtual environment to support learning activities. VLEs are commonly used to overcome the temporal and spatial restrictions of learning activities held in conventional face-to-face classrooms. In VLEs, students can participate in learning activities using the internet, and teachers can provide assistive learning tools during the process. Objective The purpose of this study was to investigate the relationships among nursing students’ mental load, cognitive load, and affective learning outcomes in terms of their willingness to serve older adults in an interaction-based educational virtual reality (VR) learning environment. Methods This study used a cross-sectional method. A total of 130 students participated in interaction-based VR learning and completed related questionnaires. Descriptive and inferential statistics and stepwise regression for data analysis were used. Results The research results revealed that in the dimension of willingness to use VR learning materials, perceived usefulness received the highest score (mean 4.42, SD 0.45). In the dimension of nursing ability, students scored the highest in information management and application ability to care for case patients (mean 4.35, SD 0.54). Correlation analysis revealed that cognitive load during learning and willingness to serve older adults were negatively correlated, whereas willingness to use VR learning materials was positively correlated with nursing ability and willingness to serve older adults. Analyzing the regression coefficients of predictor variables revealed that willingness to use VR learning materials (β=.23; t2=2.89, P=.005) and cognitive load during learning (β=–.35; t2=–.4.30, P<.001) were predictive factors of nursing students’ willingness to serve older adults. Conclusions This study demonstrated that students’ willingness to use VR learning materials and their cognitive load during learning affected their willingness to care for older adults. Therefore, the components of mental or cognitive load generate inconsistent predictive effects on affective variables and willingness to serve older adults.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".