Variability among groups of nursing students’ utilization of a technological learning tool for clinical skills training: An observational study
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 and objective: The use of technology has become the norm in nursing education. While technology has opened up for more flexible, active, student-focused teaching methods, its introduction has also brought challenges regarding its use and implementation. Recent literature has concentrated on how to best implement technology, but little attention has focused on observing student practices during technology use. Therefore, it is unknown how to optimize technology use within clinical skills training. The objective of this study was to investigate how groups of nursing students utilize a technology-based learning tool.Methods: An observational study with an exploratory design was implemented using video recordings as the data material.Results: The results indicated a high level of variability in nursing students’ performance and ability to utilize a technological tool while working in groups. The variability during clinical skills training was associated with four factors: level of competence, motivation to learn, role clarification, and collaborative problem-solving skills.Conclusions: The results of the study indicated variability in groups of nursing students’ ability to employ a technological tool during a selected procedure—namely, wound care and dressing. These findings suggest that a set of implications for faculty members should be developed. Specifically, staff and students should be prepared prior to using technology by focusing on group dynamics, group composition, development of collaborative problem-solving skills, and role modeling.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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