Predictors and students’ perceptions of NCLEX-RN success in a BS program
Bibliographic record
Abstract
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".