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Simulation with standardized patients: nursing student’s communication skills in health

2017· article· en· W2733103919 on OpenAlexaff
Fernanda dos Santos Nogueira de Góes, Natália D. Aredes, Cristina Yuri Nakata Hara, Luciana Mara Monti Fonseca, Suzanne Campbell

Bibliographic record

VenueRev Rene · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNursingPsychologyMedicineMedical education

Abstract

fetched live from OpenAlex

Objective: to identify and measure nursing student’s communication skills in health using simulation with standardized patients. Methods: descriptive, cross-sectional study carried out with 16 students of the last year of the nursing undergraduate course. The simulation focused on requesting authorization from the patient/family to perform venipuncture. During the simulation, all the students were evaluated through the Health Communication Assessment Tool. Results: of the 22 items of the tool, 18 reached agreement higher than 50.0%.Of the 16 participants, 14 (87.5%) were assessed as being able to communicate in more than 50.0% of thestatements. Conclusion: students’ communication was satisfactory; there are gaps related to the emotional aspects of the patient and the family, manifesting the need to reinforce communication in the nursing curriculum.

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)0.004
metaresearch head score (Gemma)0.019
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.513
Teacher spread0.445 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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