An exploration of ruling relations and how they organize and regulate nursing education in the high‐fidelity patient simulation laboratory
Bibliographic record
Abstract
Recently, schools of nursing have adopted the use of high-fidelity human patient simulators in laboratory settings to teach nursing. Although numerous articles document the benefits of teaching undergraduate nursing students in this way, little attention has been paid to the discourses and texts organizing this approach. This institutional ethnography uses the critical feminist sociology of Dorothy E. Smith to examine the literature and interviews with Practical and Bachelor of Science in Nursing students, and their faculty about this experience. The research shows how discourses rationalize and sustain certain processes at the expense of others. For example, ruling discourses such as biomedicine, efficiency, and the relational ontology are activated to construct the simulation lab as part of nursing and nursing education. The analysis also highlights the intended and unintended effects of these discourses on nursing education and discusses how emphasizing nursing knowledges can make the simulation lab a positive place for learning.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".