Using Logistic Regression to Investigate Self-Efficacy and the Predictors for NCLEX® Success for Baccalaureate Nursing Students
Bibliographic record
Abstract
Objectives: Ensuring success on the National Council Licensure Examination (NCLEX®) is a complex role for nurse educators. It is vital that nurse educators attain knowledge about the predictors of NCLEX success so they can design strategies and interventions to optimize student performance. Numerous studies are noted that examined the predictors for NCLEX success, reflecting great interest in this area. However, most investigated the academic predictors; few studies examined the nonacademic predictors. The purpose of this study was to identify the effect of selected academic, nonacademic, and self-efficacy variables on NCLEX outcomes to provide new knowledge to nursing science about these predictors.\nMethods: This quantitative study used Albert Bandura’s Social Learning Theory as the theoretical framework to guide its focus. Academic variables were pre-nursing scores/grades and nursing course grades, while the nonacademic variables focused on personal and environmental factors/stressors, primary language spoken, and self-efficacy expectations. A national study was conducted using an online survey. After nursing graduates (n=196) received their NCLEX scores, instruments with established reliability and validity were used to collect data about their experiences while attending school. The instruments included the (1) Recent Life Changes Questionnaire (RLCQ); (2) The Brief Measure of Worry Severity (BMWS); and (3) The General Perceived Self-Efficacy scale. Multiple logistic regression was the primary data analysis method used to identify the variables that influence NCLEX passage. Correlation analysis using Pearson product-moment correlation coefficient was also done to identify relationships existing among self-efficacy, and academic and nonacademic variables of NCLEX passage. The Chi-square test for independence was used to investigate primary language spoken and NCLEX outcome.\nResults: Logistic regression findings demonstrated that the medical-surgical grade, home and family events and responsibilities, and self-efficacy expectations were significant variables affecting NCLEX outcomes. Correlation analysis revealed that all academic variables showed a positive correlation with self-efficacy expectations, indicating that as a course grade improved, self-efficacy increased. Also, negative correlations between the nonacademic variables and self-efficacy expectations indicated that as worry or responsibilities increased for the individual, self-efficacy decreased. The Chi-square test for independence showed a significant relationship between primary language spoken and NCLEX outcome.\nConclusions: Findings imply that medical-surgical nursing courses need to be a priority in curriculum planning. Another finding demonstrates the influence of self-efficacy on NCLEX passage – the more confident a student is and the more support systems available, the better he or she will perform. This finding points to the critical need for nurse educators to study ways to increase a student’s self-confidence. The findings of this study also demonstrated that home and family events and responsibilities influence success. This knowledge may assist nurse educators to consider informing students about the need for them to seek out assistance from faculty if home and family events present obstacles to learning. Finally, it was noted that primary language spoken affects outcome. Nurse educators need to plan curricular strategies that will meet individual student needs by having a variety of support resources in place for these students.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".