Exploring Student Perspectives and Experiences of Online Opportunities for Virtual Care Skills Development: Sequential Explanatory Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Caring profession students require skills and competencies to proficiently use information technologies for providing high-quality and effective care. However, there is a gap in exploring the perceptions and experiences of students in developing virtual care skills within online environments. OBJECTIVE: This study aims to better understand caring professional students' online learning experiences with developing virtual care skills and competencies. METHODS: A sequential explanatory mixed methods approach, integrating both a cross-sectional survey and individual interviews, was used to better understand caring professional students' online learning experiences with developing virtual care skills and competencies. RESULTS: A total of 93 survey and 9 interview participants were drawn from various faculties, including students from education, nursing, medicine, and allied health. These participants identified the barriers, facilitators, principles, and skills related to learning about and delivering virtual care, including teaching methods and educational technologies. CONCLUSIONS: This study contributes to the growing body of educational research on virtual care skills by offering student insights and suggestions for improved teaching and learning strategies in caring professions' programs.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".