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Record W2136284571 · doi:10.5430/ijhe.v4n1p200

Pedagogy and Culture: An Educational Initiative in Supporting UAE Nursing Graduates Prepare for a High-stakes Nurse Licensing Examination

2015· article· en· W2136284571 on OpenAlexvenueno aff
Sharon Brownie, Ged Williams, Kate Barnewall, Suzanne Bishaw, Jennifer Cooper, Walter Robb, Neima Younis, Dawn Kuzemski

Bibliographic record

VenueInternational Journal of Higher Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
FundersGriffith University
KeywordsAbu dhabiWorkforceNurse educationNursingMedical educationMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

Graduates of an Abu Dhabi transnational nursing degree struggled with the mandatory national licensing examination. Poor pass rates undermine graduate career futures and impact on the workforce capacity building contributions of the partnering transnational educational providers. This paper describes how the design and delivery of an intensive examination preparation program dramatically reversed this trend. The objectives of this educational initiative involved the design, delivery and evaluation of a program that would align with cultural learning preferences and which improve the success rates of graduates attempting the national nurse licensing examination. To achieve these objectives, the program combined a range of teaching and assessment strategies developed to reflect the specific needs of Arabic learners, build on their existing knowledge and help them engage more effectively in the learning processes required for successful performance in a high stakes examination. Analysis of data collected during program evaluation provides useful insights into the preference and experiences of nursing graduates in the UAE Emirate of Abu Dhabi. The lessons learned are applicable to Arabic learners both regionally and globally.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.557

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 head0.124
GPT teacher head0.545
Teacher spread0.421 · 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 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

Citations6
Published2015
Admission routes1
Has abstractyes

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