Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Current Trends in Nursing II01:30

Current Trends in Nursing II

1.4K
Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
1.4K
Cancer Survival Analysis01:21

Cancer Survival Analysis

458
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
458
Current Trends in Nursing I01:28

Current Trends in Nursing I

1.7K
Current trends in nursing include:
1.7K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.9K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
5.9K
The Professional Nurse01:22

The Professional Nurse

4.9K
Professional nurses are not limited to bedside care and are taking roles of greater responsibility. A nurse should have a knowledge-based practice, including personal, theoretical, procedural, cultural, and reflexive knowledge. Additionally, nurses must be competent in cognitive, technical, interpersonal, and ethical/legal skills. Some of the best attributes of successful nurses include the following:
Communication skills: These are critical characteristics, especially speaking and listening.
4.9K
Ethical Issues01:27

Ethical Issues

1.2K
Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical Supervision and Positive Practice Environments: A Scoping Review.

Portuguese journal of public health·2026
Same author

Twenty-One Years of Insights From A Single-Center Living Kidney Donation Program.

Transplantation proceedings·2026
Same author

Cancer pain management among surgical patients in an oncology hospital in Portugal: a best practice implementation project.

JBI evidence implementation·2026
Same author

Impact of Artificial Intelligence on the Care of Terminally Ill Patients.

Healthcare (Basel, Switzerland)·2026
Same author

Person-centered practice in the Portuguese healthcare system: A documentary study.

PloS one·2026
Same author

Practices and Challenges in Portuguese Early Childhood Intervention: A Descriptive Study.

Children (Basel, Switzerland)·2026

Related Experiment Video

Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis.

Ana Rocha1,2, Cristina Costeira3,4,5, Raul Barbosa6

  • 1Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), Coimbra, 3004-011, Portugal. anamnrocha@esenfc.pt.

BMC Nursing
|July 2, 2025
PubMed
Summary

Oncology nurses with permanent contracts, work-life balance, and supportive environments show reduced burnout. Protective factors like management roles and parenthood also play a role in mitigating burnout.

Keywords:
BurnoutMachine learningOccupational healthOncology nursingProtective factorsWork environment

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Nursing
  • Oncology
  • Psychology

Background:

  • Oncology nurses face intense demands caring for patients with life-threatening illnesses.
  • Professional burnout is a significant concern in this population.
  • Identifying protective and risk factors is crucial for mitigation.

Purpose of the Study:

  • To identify burnout profiles among oncology nurses.
  • To determine socio-demographic and work-related protective patterns against burnout.

Main Methods:

  • Cross-sectional study of 150 oncology nurses in Portugal.
  • Utilized Maslach Burnout Inventory (MBI) and self-administered questionnaires.
  • Employed KMeans clustering and Random Forest machine learning algorithms.

Main Results:

  • Six protective patterns identified, including permanent contracts, work-life balance, and supportive work environments.
  • Management roles and parenthood (two or more children) showed potential protective effects.
  • Machine learning highlighted the unpredictability of burnout and the importance of protective factors.

Conclusions:

  • Resilience-building strategies and protective factors (job stability, experience, rest) are vital for reducing oncology nurse burnout.
  • Findings suggest a need for targeted, context-specific burnout prevention programs.
  • Further hypothesis-driven research is recommended for validation.