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Record W2883342252 · doi:10.5430/jnep.v8n12p36

Use of group discussion as an educational strategy during nursing appointments for patient undergoing bariatric surgery

2018· article· en· W2883342252 on OpenAlexvenueno aff
Lívia Moreira Barros, Maria Girlane Sousa Albuquerque Brandão, Amanda de Oliveira Barbosa, Ludmila Alves do Nascimento, Lorena Barbosa Ximenes, Joselany Áfio Caetano

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicWomen's cancer prevention and management
Canadian institutionsnot available
Fundersnot available
KeywordsReferralMedicineExploratory researchContent analysisNursingQualitative researchFamily medicinePsychologySociology

Abstract

fetched live from OpenAlex

Objective: The purpose of this study was to describe a health education meeting based on group discussion during a nursing appointment with patients who are waiting to perform bariatric surgery.Methods: This is an exploratory study with a qualitative approach performed in July 2017 at a referral hospital in the State of Ceará-Brazil in the performance of bariatric surgeries. Twelve subjects participated and the data collection took place through self-completion of a semi-structured interview. Data analysis was performed according to the content analysis proposed by Bardin (2009).Results: It was observed that the participants considered that the group discussion allows interaction among the group and favors the construction of common knowledge.Conclusions: It was concluded that group discussion is a favorable methodology to be used during health education in nursing appointments, because it allows the sharing of doubts and experiences among the participants.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.416
Teacher spread0.335 · 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 designOther design
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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