Common variables found among students who were unsuccessful on the NCLEX-RN® in a baccalaureate nursing program
Bibliographic record
Abstract
Schools of nursing (SON) must meet the challenges of producing safe, competent practitioners. Educators are constantly trying to identify predictors of program completion and National Council Licensure Examination–Registered Nurses (NCLEX-RN®) success, as well as variables that put students at risk for failure. The purpose of this study was to determine common variables among students from a baccalaureate-nursing program who were unsuccessful in the nursing program or on the NCLEX-RN®. This cross sectional research study utilized a retrospective correlational design to discover the relationships between independent variables of degree and cumulative GPAs, specific courses repeated, number of repeated courses and whether the student had full-time or part-time clinical faculty members and the independent variables program non-completion and NCLEX-RN® failure. The theoretical underpinning that guided this study was Bandura’s Social-Cognitive Theory of Self-Efficacy. Data analyses were conducted using a series of crosstabulations with chi-square analysis and t-tests. The research questions were investigated using binary logistic regressions. The relationship between repeated chemistry courses and NCLEX-RN® examination success was significant. Relationships between repeated English, math, chemistry and other science courses and nursing program failure were significant. Cumulative GPAs were significantly lower for all groups analyzed. Two binary logistic regression analyses were conducted to determine variables that would predict students who failed to complete the program or failed the NCLEX-RN®. Overall both models were significant. Results may be utilized to modify admission requirements and admit students that have a higher probability of being successful in the nursing program and on the NCLEX-RN®.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".