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Record W4206525081 · doi:10.5430/jnep.v12n5p41

The lived experience of new graduate nurses successfully completing NCLEX-RN

2021· article· en· W4206525081 on OpenAlexvenueno aff
Shravan Devkota, Collette Loftin, Holly Jeffreys

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)CoachingMedical educationPsychologyTest (biology)PedagogyMedicineEngineering

Abstract

fetched live from OpenAlex

Background and objective: The NCLEX pass rate is considered the primary indicator of program quality. Much literature exists regarding pre-graduation efforts aimed at aiding students to prepare for the NCLEX-RN exam, while there is little available on post-graduation efforts. This project was conducted to identify the post-graduation experiences of successful NCLEX-RN test takers as they prepared to complete the exam.Methods: This was a qualitative descriptive study utilizing a phenomenological framework to determine the lived experience of new graduates preparing to complete the NCLEX-RN exam.Results: Four main themes were identified as relevant to post graduation experiences including: (a) Finding Motivation, (b) Study Tactics, (c) Taking a Break, and (d) The Testing Experience. Additionally, few of the participants took it for granted that they were going to pass the exam, they reported wishing they had spent more time preparing, and with regard to studying, several described wishing they had started earlier.Conclusions: It will be beneficial for faculty to discuss potential strategies for success to utilize after graduation, including expectations of testing day, setting a realistic timetable to test, overcoming lack of motivation to preparation for the exam, and careful scheduling of coaching and study sessions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.243
GPT teacher head0.494
Teacher spread0.251 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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