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Record W4319264647 · doi:10.5430/jct.v12n1p176

Comparison of the Effects of Simulation Practice and Clinical Practice Education: Nursing Care of Children with Respiratory Diseases

2023· article· en· W4319264647 on OpenAlexvenueno aff
Ju Hee Hwang

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsnot available
FundersKyungdong University
KeywordsClinical PracticeMedicineCompetence (human resources)NursingNursing practiceTest (biology)Family medicinePsychology

Abstract

fetched live from OpenAlex

This study This study aimed to find the efficient practice education measures for the practice of pediatric health nursing by comparing the effects of simulation practice education and existing clinical practice one about children with respiratory diseases. Using the nonequivalent comparison group post-test non-synchronized design method, the research subjects were composed of total 62 people including 32 subjects for experimental group and 30 subjects for comparison group. Using the IBM SPSS v. 25.0 for data analysis, the differences in the baseline characteristics of both groups were tested through the t-test, X2-test, and Fisher’s exact test. The results of this research are as follows. In the results of conducting the homogeneity test on the experimental group and comparison group, there were no differences between two groups while it was statistically significant in self-efficacy. The experimental group who participated in practice education using the simulator was statistically significant in nursing competence(F=27.183, p<.001), communication skills(F=7.876, p=.001), and learning satisfaction(F=12.950, p<.001). The problem-solving ability(F=2.515, p=.089) was not statistically significant. Such results are significant in the aspect of implying the possibility of practice education using the simulator that could effectively complement clinical practice in the practice education of pediatric health nursing in the future.

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)0.006
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 score0.160
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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 head0.023
GPT teacher head0.463
Teacher spread0.439 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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