Theoretical Framing of High-Fidelity Simulation With Carper’s Fundamental Patterns of Knowing in Nursing
Bibliographic record
Abstract
Many nursing programs integrate high-fidelity simulation(HFS) into the curriculum. The manikins used are modeled to resemble humans and are programmed to talk and reproduce physiological functions via computer interfaces.When HFS design negates a theoretical framework consistent with the interpersonal and relational nature of nursing,it can problematically focus simulation on psychomotor skills and the physical body. This article highlights a theorized approach to HFS design informed by Carper's seminal work on the fundamental patterns of knowing in nursing(i.e., empirics, esthetics, personal knowing, and ethics). It also describes how a team of Canadian nurse educators adopted these patterns of knowing as a theoretical lens to frame scenarios, learning objectives, and debriefing probes in the context of maternal and newborn assessment. Institutions and practitioners can draw on Carper's work to facilitate focusing on the whole person and expanding the epistemological underpinnings of HFS in nursing and other disciplines.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.056 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".