Affectivity in Screen Based Simulation in Nursing Education: An Interpretive Description Study
Bibliographic record
Abstract
The expanded adoption of screen-based virtual simulation in nursing education was a response to the COVID-19 pandemic to ensure the continuity of nursing instruction. This change in instructional approach provided an opportunity to explore the experiences of nursing students who have had increased exposure to this teaching method. Nursing simulation creates a learning experience that is both engaging and meaningful for students, helping them connect theory with real-world clinical practice. The purpose of this study was to explore nursing students' experiences with affective learning through the framework of embodied cognition, suggesting that this perspective offers a more complete way of understanding the learning process as being inherently affective. The research aimed to assess affective learning within the existing screen-based simulation curriculum and to explore how nursing students perceive and engage with affective learning in these virtual environments. Additionally, the concept of embodied cognition was utilized to provide insights into how nursing students develop their affective skills through virtual simulations. This study adopted a qualitative interpretive description approach to gather data from eight undergraduate nursing students across two Canadian universities. Participants completed digital reflective journals to document their experiences after each screen-based simulation scenario. Follow up, semi-structured interviews were conducted and recorded via Zoom. All interview transcripts and journal entries were transcribed verbatim. A constant comparative and thematic analysis process identified four primary themes: communication, realism, affective connections, and personal learning experiences. The findings suggest that the current simulation design stimulates affective learning in ways that are not captured in the conventional idea of a confined “affective domain.”, potentially limiting opportunities for students to engage in meaningful affective learning experiences. Exploring nursing students’ affective experiences of learning offered valuable insights and illustrative examples of affective learning occurring even in the absence of explicitly articulated affective objectives, thereby underscoring the idea that all learning is intrinsically affective. The theoretical framework of embodied cognition provides a novel perspective in nursing education on understanding and articulating the affective learning processes that occur during interactions with virtual patients in screen-based simulation.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".