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Record W2798061827 · doi:10.15446/rsap.v19n4.44319

A province-wide survey on self-reported language proficiency and its influence in global health education

2017· article· es· W2798061827 on OpenAlexaffabout
Mirella Veras Mirella, Kevin Pottie, Vivian Welch, Javier Eslava‐Schmalbach, Peter Tugwell

Bibliographic record

VenueRevista de Salud Pública · 2017
Typearticle
Languagees
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsLanguage barrierHealth careNursingPsychologyObservational studyMedical educationGlobal healthCross-sectional studyMedicinePublic healthPolitical science

Abstract

fetched live from OpenAlex

Objetivo De acuerdo con la literatura, el idioma es el obstáculo más común en el contexto de la atención médica y un factor de riesgo asociado con resultados negativos. El objetivo de este estudio es presentar las diferencias percibidas entre los estudiantes de enfermería que hablan un idioma y aquellos que hablan dos o más (competencia lingüística reportada por ellos mismos) y sus habilidades y necesidades de aprendizaje en salud global.Método Estudio observacional de corte transversal entre estudiantes de enfermería de cinco universidades de Ontario. Se diseñó una encuesta para medir el conocimiento, las habilidades y las necesidades de aprendizaje en salud global.Resultados Se observó que los estudiantes que hablan más de dos idiomas tienen mayor probabilidad de interesarse más en aprender sobre problemas de salud global, los riesgos para la salud y su asociación con los viajes y la migración (p=0,44), así como sobre los determinantes sociales de la salud (p=0,042).Conclusión Es necesario que se brinde capacitación en aprendizaje de otros idiomas a los estudiantes de enfermería para que puedan afrontar las barreras impuestas por el lenguaje en los contextos de atención médica y mejorar la salud global, de manera local e internacional.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.037
GPT teacher head0.416
Teacher spread0.379 · 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.

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

Citations3
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueRevista de Salud PúblicaSame topicCultural Competency in Health CareFrench-language works237,207