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Record W4405175823 · doi:10.3928/01484834-20240724-01

Understanding NCLEX-RN Repeat Writers' Learning Needs: A Pilot Developmental Design Research Study

2024· article· en· W4405175823 on OpenAlexaffabout
Marnie Kramer

Bibliographic record

VenueJournal of Nursing Education · 2024
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFormative assessmentPsychologyMedical educationTest (biology)NursingPedagogyMedicine

Abstract

fetched live from OpenAlex

Background Graduates who fail the National Council Licensing Examination for Registered Nurses (NCLEX-RN) require evidence-informed educational support. Method This educational development study tested an online program created by a nurse educator and researcher for Canadian-educated nursing students ( n = 19) who were taking the NCLEX-RN for the second time. Data collected were biweekly virtual meetings with detailed field notes, program completion data, and self-reported NCLEX-RN results. Formative evaluation of the online program was completed after each of the three research cycles. Results Ten students passed and two students failed the next examination, five students were lost to follow-up, and two students withdrew from the study. Participants had challenges completing the online program. Two types of repeat writers emerged and included students who had an under or over attention to their studying during remediation. Given these findings, specific education design principles are suggested for repeat NCLEX-RN test takers. Conclusion Students who take the NCLEX-RN a second time require combined cognitive and affective learning supports to combat the stress of failing. [ J Nurs Educ . 2025;64(2):121–124.]

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)0.004
metaresearch head score (Gemma)0.000
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 score0.717
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.410
GPT teacher head0.466
Teacher spread0.056 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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