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Record W1976495876 · doi:10.3928/01484834-20121217-02

Theoretical Framing of High-Fidelity Simulation With Carper’s Fundamental Patterns of Knowing in Nursing

2012· article· en· W1976495876 on OpenAlexaffabout
Barb McGovern, Jennifer Lapum, Laurie Clune, Lori Schindel Martin

Bibliographic record

VenueJournal of Nursing Education · 2012
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDebriefingFraming (construction)FidelityCurriculumInterpersonal communicationPsychomotor learningPsychologyComputer scienceNursingEngineering ethicsPedagogyMedicineSocial psychologyCognition

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.429
Teacher spread0.393 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2012
Admission routes2
Has abstractyes

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