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Record W3170804635 · doi:10.5430/jnep.v11n10p26

Knowledge of students from a health science university on human error and patient safety

2021· article· en· W3170804635 on OpenAlexvenueno aff
Iohanna A. Paiva, Bruna Michelle Belém Leite Brasil, Naiana P. Alvez, Káren Maria Borges Nascimento, George Jó Bezerra Sousa, Maria Lúcia Duarte Pereira, Rhanna Emanuela Fontenele Lima de Carvalho

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyPatient safetyTheme (computing)PsychologyNursingMedical educationMedicineHealth careComputer sciencePathology

Abstract

fetched live from OpenAlex

Objective: This research aimed to identify the knowledge of students from a health science university on human error and patient safety.Methods: This is an observational, cross-sectional research with a quantitative approach. A total of 228 students of the following baccalaureate courses participated: Physical Education, Nursing, Medicine, Nutrition, and Psychology. The study was conducted in the second semester of 2019 through an online questionnaire with 27 closed and 4 open questions. Simple frequencies, central tendency measures, and correlation tests were used to analyze the data. The IRAMUTEQ software was used to analyze the answers to open questions.Results: The study demonstrated that the Nursing students had more contact with the patient safety theme and greater confidence to perform techniques during clinical practice. In general, students pointed out positive responses regarding knowledge about patient safety, such as: recognition of the possibility of errors, the importance of communication, and learning from mistakes.Conclusion: The results may contribute to strengthening teaching about patient safety in universities.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.238
GPT teacher head0.598
Teacher spread0.360 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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