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Record W2916859914 · doi:10.5430/jnep.v9n6p32

Predictors and students’ perceptions of NCLEX-RN success in a BS program

2019· article· en· W2916859914 on OpenAlexvenueno aff
Hee Jun Kim, Teresa Nikstaitis, Hyun-Jeong Park, Lorraine Armstrong, Hayley Mark

Bibliographic record

VenueJournal of Nursing Education and Practice · 2019
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsLicensureDescriptive statisticsLogistic regressionPerceptionNursingPsychologyMedical educationDescriptive researchHealth careMedicinePolitical science

Abstract

fetched live from OpenAlex

Background: First time National Council Licensure Examination-Registered Nurses (NCLEX-RN) pass rates and successful student progression in a program are considered key indicators of quality of nursing programs. The purpose of this study was to investigate the predictors of first-attempt NCLEX-RN success among multiple factors, and to explore the students’ perception for NCLEX-RN.Methods: A retrospective descriptive design was used including a total of 671 students who were admitted as a junior to the program between spring 2012 and fall 2015. Descriptive statistics and multiple logistic regression models were conducted to find significant predictors of first time NCLEX-RN success.Results: Course grades for adult health, family health, critical care health, and the repeated course history, and HESI scores for adult health, family health, and the EXIT exam were significant predictors of NCLEX-RN success. Students perceived that the review course and practice test were helpful in passing NCLEX-RN.Conclusions: Findings of this study would be beneficial for nursing programs to strategize effectively for students who are at risk of failing and support them in their NCLEX-RN preparation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.048
GPT teacher head0.444
Teacher spread0.396 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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