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Record W1995593239 · doi:10.5430/jnep.v4n9p155

Developing critical thinking skills in undergraduate nursing students: The potential for strategic management simulations

2014· article· en· W1995593239 on OpenAlexvenueno aff
Karen LaMartina, Peggy Ward‐Smith

Bibliographic record

VenueJournal of Nursing Education and Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCritical thinkingGraduation (instrument)FidelityNurse educatorMedical educationOrder (exchange)NursingStrategic thinkingNurse educationPsychologyMedicinePedagogyStrategic planningComputer scienceEngineeringManagement

Abstract

fetched live from OpenAlex

Critical thinking is a skill required by nurses in order for sound clinical decisions to be made with optimal patient outcomes. Previous nursing education specific strategies, aimed at developing critical thinking, include high-fidelity simulation, case study discussions, concept-mapping, and reflective writing. Post-graduation nurse residency programs have had some success in preparing the new graduate nurse to take on the challenges and demands of the profession. These strategies have had varied success in developing critical thinking skills and are, at times, costly. The purpose of this article is to explore Strategic Management Simulations (SMS) as an educational strategy for enhancing the development of critical thinking skills among undergraduate nursing students. SMS includes assessment, feedback, and training, which are not included in other strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.083
GPT teacher head0.492
Teacher spread0.409 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations34
Published2014
Admission routes1
Has abstractyes

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