Exploring the Impact of Virtual Reflection Groups on Advanced Practice Nurse Students During the COVID-19 Pandemic: Focus Group Study With Master’s Students
Bibliographic record
Abstract
BACKGROUND: In the master's program of advanced practice nursing at a Norwegian university college, the learning activity reflection groups were converted into virtual reflection group (VRG) meetings during the COVID-19 pandemic. Regardless of the students' clinical practices in different hospitals, they could participate in the same VRG meeting on the web together with the educator from the university college, and the clinical supervisors were invited to participate. The students were in the process of developing the core competence required in their role as advanced practice nurses (APNs), and they had increased responsibility in the implementation of the VRG meetings. OBJECTIVE: In this study, we aimed to explore how master's students of advanced practice nursing experienced VRG meetings during the COVID-19 pandemic. METHODS: A qualitative exploratory design was adopted using focus group interviews. A group of students in the master's program of advanced practice nursing participated in an interview that lasted for 60 minutes. They had experienced participating in the VRG meetings following a rigorous guide during their clinical practice. The data from the focus group were analyzed using qualitative content analysis. RESULTS: The main findings of this study highlighted the importance of structure in VRG meetings, the role of increased responsibility in students' learning processes, the development of APN students' competencies, and increased professional collaboration with clinical supervisors. The APN students and clinical supervisors also continued their discussions in the clinical setting afterward, which strengthened the collaboration between students' education in the master's program and their clinical practice. CONCLUSIONS: VRG meetings gave the students the opportunity to lead professional discussions while reflecting thoroughly on the chosen patient cases from clinical practice. They experienced receiving feedback from fellow students, supervisors, and educators as stimulating their critical thinking development.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".