Using Logistic Regression to Investigate Self-Efficacy and the Predictors for NCLEX® Success for Baccalaureate Nursing Students
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».