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Record W4212982887 · doi:10.17483/2368-6669.1295

Comparative Study of Knowledge Acquisition, Satisfaction, Self-Confidence and Perceived Support in Nursing Students Experiencing Simulation Versus Clinical Placement in Perinatal Care

2022· article· en· W4212982887 on OpenAlexaffvenue
Catherine Pépin, Marilyn Aita, Andréane Lavallée, Johanne Goudreau

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2022
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsKnowledge acquisitionNursingPsychologyConfidence intervalTest (biology)GeneralizationNursing careMedicineComputer scienceKnowledge managementInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study was to compare nursing students experiencing simulations (SIM group) (n = 25) versus clinical placement (CPG) (n = 55) in perinatal care. Questionnaires on satisfaction, self-confidence, and perceived support were completed by both groups. Knowledge acquisition was assessed using standardized course evaluation. The Student’s t-test showed that differences between groups were not statistically significant for knowledge acquisition and satisfaction, while they were statistically significant for self-confidence and perceived support with higher scores in the CPG group. This study contributes to knowledge development since few have compared simulations as a learning method to replace totally clinical placement. Findings support the simulations as an appropriate method for students’ knowledge acquisition and satisfaction in a perinatal care course, but more studies are needed for generalization.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.523
Teacher spread0.444 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

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