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Record W2791021042 · doi:10.3917/rsi.131.0071

Une simulation interprofessionnelle : une exploration des expériences des étudiantes en sciences infirmières

2018· article· fr· W2791021042 on OpenAlexaff
Michelle Lalonde, Marie-Christine Malouin-Benoit, Elizabeth Gagnon, Alain Michon, Monique Maisonneuve, Janie Desroches

Bibliographic record

VenueRecherche en soins infirmiers · 2018
Typearticle
Languagefr
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMontfort HospitalUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

BACKGROUND: the literature suggests that simulation is an effective strategy to meet the learning needs of nursing students. Traditionally, simulation learning for nursing students takes place at nursing schools ; at a distance from the clinical setting, patients, and the interprofessional team. AIM: the objective of this pilot project is to explore the experiences of Francophone nursing students following their participation in an interprofessional simulation in a hospital setting during their third year clinical placements. METHOD: a case study using Yin's (2003) approach was used to explore this phenomenon through focus groups and individual interviews. RESULTS: thirteen people participated in three simulation sessions that each included two scenarios. Content analysis of the focus groups revealed four themes : 1) the need for a realistic, but safe environment ; 2) simulation helps to build self-confidence ; 3) simulation improves knowledge of the role of the nurse ; and 4) simulation improves knowledge of teamwork. Two themes emerged from individual interviews : 1) the knowledge and skills acquired during the simulation were retained over time ; and 2) perceptions of the effects on the quality and safety of patient care. CONCLUSION: the use of simulation could be effective for the development of knowledge of nursing role, teamwork, and self-confidence.

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)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0020.005
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.387
GPT teacher head0.541
Teacher spread0.154 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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