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Record W4380088894

Conocimiento acerca del proceso de consentimiento informado en investigación en salud en estudiantes de medicina

2017· article· es· W4380088894 on OpenAlexaff
Rebeca Mancilla, Jenny G. López-Godínez, Carmen I. Vilagrán, Brooke M. Ramay, Renata Mendizabal, Aida G. Barrera

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languagees
FieldMedicine
TopicEthics and bioethics in healthcare
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

La importancia del proceso de consentimiento informado en bioética es asegurar el respeto de los derechos y seguridad de los participantes en una investigación en salud. Se generó un cuestionario con el objetivo de determinar el conocimiento acerca del proceso de consentimiento informado en investigación en salud, en 461 estudiantes de segundo a sexto año de la Carrera de Médico y Cirujano de la Facultad de Ciencias Médicas, USAC, durante octubre a noviembre de 2016. Se evaluaron los conocimientos sobre conceptos y aplicación de los principios bioéticos, la importancia y elementos que forman el proceso de consentimiento informado en investigación en salud a través de un cuestionario electrónico vía internet (un tema por serie, cuatro series, preguntas de selección múltiple por tema; 18 preguntas); se evaluó como suficiente (? 61% de respuestas correctas) o insuficiente (&lt; 61% de respuestas correctas). El mayor acierto de los estudiantes a las preguntas fue sobre práctica en ética, aplicación<br />de principios bioéticos, 89%; e importancia del proceso de consentimiento informado, 94%, y el menor acierto en preguntas sobre teoría de ética, concepto de los principios bioéticos, 70%.

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)0.017
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0070.004
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.408
GPT teacher head0.640
Teacher spread0.232 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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