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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,...
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Advancing Data Science Competencies for Nursing PhD Students.

Suzanne Bakken1, Caitlin Dreisbach2, Sergey Kisselev1

  • 1Columbia University, New York, New York, USA.

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|July 25, 2024
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A new data science course for PhD nursing students enhanced their skills using big data and AI tools. Students showed improved data science competencies after completing the 15-week program.

Keywords:
Educationdata sciencegenerative AIvisualization

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Area of Science:

  • Nursing Research
  • Data Science Education
  • Artificial Intelligence in Healthcare

Background:

  • The increasing significance of data science in nursing research necessitates specialized training.
  • Existing PhD curricula may not adequately cover advanced data science methodologies.
  • There is a growing need to integrate big data analytics and AI into nursing scholarship.

Purpose of the Study:

  • To develop and implement a data science course within a PhD nursing program.
  • To equip future nurse scientists with essential data science and AI competencies.
  • To assess the impact of the course on students' self-perceived data science abilities.

Main Methods:

  • A 3-credit, 15-week course was integrated into the second-year PhD curriculum at Columbia University School of Nursing.
  • Students engaged with a big data source, Jupyter Notebook, and R programming language to address a research question.
  • Generative AI tools were incorporated into the course in 2023.

Main Results:

  • Student self-evaluations indicated an improvement in data science competencies from baseline.
  • The course provided practical experience in applying data science techniques to nursing research questions.
  • The integration of AI tools reflected the latest advancements in the field.

Conclusions:

  • The developed data science course effectively enhances PhD nursing students' competencies.
  • This case study contributes valuable insights into data science and AI education in nursing.
  • The curriculum provides a model for other institutions seeking to bolster data science training in nursing.