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Record W2947885808 · doi:10.5539/elt.v12n7p1

Identifying Core Learning Needs for English for Nursing Purposes

2019· article· en· W2947885808 on OpenAlexvenueno aff
Chi-Ying Chien

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologySituational ethicsReading (process)NursingSentenceMedical educationMedicineComputer scienceLinguisticsNatural language processing

Abstract

fetched live from OpenAlex

The purpose of this study is to explore the core learning needs being met from English exercises that are most helpful for Taiwanese nursing preprofessionals and in-service full-time nurses. The study focuses on the learning exercises that are most useful in the workplace, lists the five exercises used most and least frequently in school, compares exercises in terms of different proficiency levels, and refines the most important training components as common core learning needs for preprofessionals and in-service nurses. Subjects were randomly selected, and 60 female nursing majors were surveyed in a questionnaire about 18 learning exercises most teachers use in the classroom. Data was interpreted using a one-way analysis of variance. Then an a posteriori comparison was performed for those items reaching significance to gather advanced information on where the significance originates from. Based on the findings, the following common core learning needs have been identified: cultural notes for nursing, building medical vocabulary, and short expressions and medical information. The intended result of this study is to apply general English exercises such as vocabulary introduction, sentence patterns, and situational expressions to English for medical purposes exercises such as cultural notes for nursing, improving medical vocabulary, and reading medical journals for the benefit of both English for nursing purposes teachers and learners.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.060
GPT teacher head0.451
Teacher spread0.391 · 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 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

Citations6
Published2019
Admission routes1
Has abstractyes

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