Identifying predictors of nursing dropout and attrition before and after Bachelor's Graduation based on the IPOD model: A machine learning approach
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| Vydáno v: | Nurse Education in Practice vol. 88 (Oct 2025), p. 104580-104597 |
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Elsevier Limited
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| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1016/j.nepr.2025.104580 |2 doi | |
| 035 | |a 3270292467 | ||
| 045 | 2 | |b d20251001 |b d20251031 | |
| 084 | |a 170342 |2 nlm | ||
| 100 | 1 | |a Arian, Mahdieh |u Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran | |
| 245 | 1 | |a Identifying predictors of nursing dropout and attrition before and after Bachelor's Graduation based on the IPOD model: A machine learning approach | |
| 260 | |b Elsevier Limited |c Oct 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Aim To identify predictors of academic dropout before graduation and professional attrition after graduation among nursing students, using the theoretical IPOD model and a machine learning approach. Background Academic dropout and professional attrition are global challenges. Design A retrospective study design. Method This study included 878 undergraduate nursing students enrolled between 2007 and 2018. Data were collected from Education Department records and follow-up interviews conducted via phone, email, or social media platforms. To predict academic dropout before graduation, four machine learning models were used: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree (DT), and Multinomial Logistic Regression (mLR). To predict professional attrition after graduation, Random Forest (RF), SVM, DT, and Binary Logistic Regression (BLR) models were applied. Outcomes The academic dropout rate was 2.2 %, while the professional attrition rate was 28.3 %. The XGBoost model, with 91 % accuracy, identified dropout predictors including a higher ratio of failed semesters to total semesters, lower GPA in the first and second semesters, younger age at admission, abnormal early academic status, tuition payment, and male gender. The Random Forest model, with 90 % accuracy, linked professional attrition to higher clinical competency, higher overall GPA, longer waiting time before employment, job burnout, and longer work experience. Conclusions Academic performance indicators, particularly during the early semesters, were associated with nursing student dropout, while professional factors such as job burnout and employment delays were linked to post-graduation attrition. These findings may inform targeted interventions to improve retention across both academic and professional stages. | |
| 651 | 4 | |a Iran | |
| 653 | |a Academic achievement | ||
| 653 | |a Students | ||
| 653 | |a Email | ||
| 653 | |a Attrition | ||
| 653 | |a Shortages | ||
| 653 | |a College students | ||
| 653 | |a Employment | ||
| 653 | |a Professional development | ||
| 653 | |a Burnout | ||
| 653 | |a Performance indicators | ||
| 653 | |a Work experience | ||
| 653 | |a Entrance examinations | ||
| 653 | |a Social media | ||
| 653 | |a Workforce | ||
| 653 | |a Nurses | ||
| 653 | |a Algorithms | ||
| 653 | |a Learning | ||
| 653 | |a Midwifery | ||
| 653 | |a Accuracy | ||
| 653 | |a Nursing education | ||
| 653 | |a Dropping out | ||
| 653 | |a Grades (Scholastic) | ||
| 653 | |a Digital audio players | ||
| 653 | |a Nursing | ||
| 653 | |a Work | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision making | ||
| 653 | |a Nursing care | ||
| 653 | |a Nursing schools | ||
| 653 | |a Data collection | ||
| 653 | |a Tuition | ||
| 653 | |a Admissions policies | ||
| 653 | |a Career advancement | ||
| 653 | |a Regression analysis | ||
| 653 | |a Attrition (Research Studies) | ||
| 653 | |a Doctoral Programs | ||
| 653 | |a Academic Records | ||
| 653 | |a Graduation | ||
| 653 | |a Job Satisfaction | ||
| 653 | |a Artificial Intelligence | ||
| 653 | |a Graduate Surveys | ||
| 653 | |a Dropouts | ||
| 653 | |a Career Change | ||
| 653 | |a Individual Characteristics | ||
| 653 | |a Content Validity | ||
| 653 | |a Educational Quality | ||
| 653 | |a Undergraduate Students | ||
| 653 | |a Graduates | ||
| 653 | |a Learning Experience | ||
| 653 | |a Environmental Influences | ||
| 653 | |a Intention | ||
| 653 | |a Data Analysis | ||
| 653 | |a Labor Force Development | ||
| 653 | |a Educational Experience | ||
| 653 | |a College Science | ||
| 700 | 1 | |a Kamali, Azadeh |u Department of Nursing, Bojnurd Faculty of Nursing, North Khorasan University of Medical Sciences, Bojnurd, Iran | |
| 700 | 1 | |a Dalir, Zahra |u Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran | |
| 700 | 1 | |a Hajiabadi, Fatemeh |u Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran | |
| 700 | 1 | |a Mazloum, Seyed Reza |u Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran | |
| 773 | 0 | |t Nurse Education in Practice |g vol. 88 (Oct 2025), p. 104580-104597 | |
| 786 | 0 | |d ProQuest |t Sociology Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3270292467/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3270292467/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3270292467/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |