Variability among groups of nursing students’ utilization of a technological learning tool for clinical skills training: An observational study
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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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".