# Predicting nurse burnout: A logistic regression approach to workplace and demographic factors

## TL;DR

This study identifies factors like workload and job satisfaction that contribute to nurse burnout in Albania, suggesting organizational changes to improve nurse well-being.

## Contribution

The study introduces a logistic regression model to predict nurse burnout based on workplace and demographic factors in a specific regional context.

## Key findings

- High workload and long shifts significantly increase nurse burnout risk.
- Rural nurses have 57% higher odds of burnout compared to urban nurses.
- Logistic regression accurately classified 72% of nurses by burnout level.

## Abstract

This study aimed to identify key occupational and demographic factors associated with nurse burnout in a major public hospital in Albania.

A descriptive cross-sectional survey was conducted among nurses in a regional hospital. Nursing management invited all units to participate. Nurses completed the questionnaire voluntarily and anonymously during breaks. The survey included job satisfaction, burnout risk, working conditions, supervisor and colleague support, workload, shift duration, career opportunities, and demographic variables. Cluster analysis was used to categorize nurses, and exploratory factor analysis examined the structure of job satisfaction factors.

Data from 345 nurses showed that high workload and long shifts significantly increased burnout risk. Strong supervisor support and greater job satisfaction were associated with reduced burnout. Nurses in rural settings had 1.57 times higher odds of burnout compared to urban nurses. Female nurses had 1.25 times greater odds of burnout than male nurses. Advanced education and better career advancement opportunities were linked to lower burnout levels.

Burnout is more prevalent among rural nurses and, to a lesser extent, among female nurses, suggesting the need for context-sensitive and inclusive interventions. Burnout stems from systemic challenges such as excessive workload, insufficient managerial support, and role misalignment. Addressing these issues requires organizational changes including staffing improvements, supportive leadership, and professional development. Future research should apply standardized burnout measures and longitudinal approaches to better understand nurse well-being.

•High workload and long shifts significantly increase nurse burnout risk.•Job satisfaction and supervisor support strongly reduce burnout levels.•Nurses in rural hospitals show 57 % higher burnout odds than urban nurses.•Higher education levels are linked to lower burnout and greater resilience.•Logistic regression classified 72 % of nurses accurately by burnout level.

High workload and long shifts significantly increase nurse burnout risk.

Job satisfaction and supervisor support strongly reduce burnout levels.

Nurses in rural hospitals show 57 % higher burnout odds than urban nurses.

Higher education levels are linked to lower burnout and greater resilience.

Logistic regression classified 72 % of nurses accurately by burnout level.