Group Dynamics as a Component of the Clinical Learning Environment: A Mixed Sequential Exploratory Study
Bibliographic record
Abstract
Context and Problem Statement: In the clinical learning environment (CLE), various components influence the experiential learning of nursing students, including group dynamics in a group practicum modality. To date, the scientific literature does not fully explore the impact of this component in clinical education, hindering the establishment of optimal conditions for student learning. The purpose of this study was to explore group dynamics among nursing students as a component of the CLE within the group practicum modality. Objectives: 1) To describe group dynamics in relation to learning based on existing literature and from the perspective of nursing students, clinical supervisors, and nursing staff, 2) to adapt the Évaluation de l’environnement d’apprentissage clinique (EEAC) instrument to include the concept of group dynamics, 3) to explore the components of CLE, including group dynamics, among baccalaureate nursing students at a francophone university in New Brunswick, and 4) to explore the links between group dynamics and other components in the CLE recognized in the literature. Methodology: We used a mixed sequential exploratory design. Phase 1: Scoping review followed by a descriptive interpretive approach. Phase 2: Adaptation of the EEAC instrument. Phase 3: Cross-sectional descriptive study. Results: Several student characteristics and stress-related factors influence group dynamics and the learning experience. Peer support enhances interpersonal relationships among group members and contributes to the learning experience. Support from nursing staff positively impacts group dynamics and student learning experiences. Additionally, supervision by clinical instructors significantly explains group dynamics. Conclusion: Recognizing the crucial role of group dynamics as an influential component in the CLE, parties involved in nursing student education now have conclusive results. This information enables them to make significant contributions to creating an optimal learning environment for students.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".