Empathy and High-Fidelity Human Patient Simulators: A Critical Analysis of Undergraduate Nursing Education
Bibliographic record
Abstract
This paper critically examines the role of high-fidelity human patient simulators in undergraduate nursing education, particularly for empathy development. While high-fidelity human patient simulators are instrumental for clinical skills practice, they fall short of adequately fostering empathy. Underlining the centrality of empathy in nurse–patient relationships for positive health outcomes, blended learning approaches integrating role-play with human-to-human interactions are suggested. In this discussion paper, the historical perspectives on empathy, the challenges in measuring and developing empathy, the impact of the pandemic-induced shift to simulation-based clinical training, and the influence of neoliberal values on nursing education are examined. We explore research that highlights the importance of authentic patient engagement and calls for human-centric approaches in simulation pedagogical approaches. We suggest the need for balanced educational strategies prioritizing authentic human interactions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Discussion paper critiquing high-fidelity simulation for empathy development in undergraduate nursing education; the object is pedagogy and professional training for practice, not research practice.
This discussion concerns simulation-based nursing education and empathy, not research itself.
Critique of simulators in undergraduate nursing education for empathy; professional pedagogy, not research-as-object.
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 | 0.030 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".