The social-psychological process involved in using human patient simulators as a teaching/learning modality in undergraduate nursing education
Bibliographic record
Abstract
The use of the high-fidelity human patient simulator (HPS) based clinical scenario in undergraduate nursing education is a powerful learning tool well suited to modern students’ preference for immersive construction of knowledge through the provision of contextually rich reality-based practice and social discourse. To date there has been little indication of research into the social processes in which students engage in a simulated clinical session. The purpose of this paper-based thesis was to explore these social-psychological processes that occur within HPS-based clinical scenarios to inform nurse educators’ choice of pedagogical practices when they structure and implement this technology-based learning tool. This exploration began with the first manuscript, which explores this approach to clinical teaching through a critical examination of the application of behaviorist and constructivist pedagogy to high-fidelity scenario-based simulation sessions. The second manuscript critically analyzes the role of clinical scenarios using human patient simulation in promoting transformative learning events in undergraduate nursing education. The third manuscript begins with the assertion that HPS-based learning experiences are in reality social endeavors that serve as a platform for social discourse among learning groups and follows with an analysis of the theoretical and philosophical foundations of the grounded theory research method, demonstrating its suitability to uncovering the social processes within. Finally, the dissertation process culminated in the fourth manuscript, which is a report on a grounded theory study that explored the social-psychological processes that occur within HPS-based clinical scenarios. This study sampled students and faculty from a Western Canadian baccalaureate nursing program. The data collection consisted of semistructured interviews, supplemented by secondary data from the observation of participants as they engaged in HPS-based clinical scenarios, field notes, analytical and operational memos, and journaling. The process of leveled coding generated a substantive theory that has the potential to enable educators to empower students through the use of fading support, a twofold process comprised of adaptive scaffolding and dynamic assessment that challenges students to realistically self-regulate and transform their frame of reference for nursing practice, while at the same time limiting the threats that traditional HPS-based curriculum can impose.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".