Understanding NCLEX-RN Repeat Writers' Learning Needs: A Pilot Developmental Design Research Study
Bibliographic record
Abstract
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.]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".