Impact of ChatGPT-based Nursing Process Training

August 14, 2024 updated by: Belgin Varol, Saglik Bilimleri Universitesi

The Impact of Nursing Students' Clinical Decision-making Competencies and Artificial Intelligence Anxiety on ChatGPT-based Nursing Process Training.

The goal of this randomized controlled study is to determine the impact of ChatGPT based nursing process training on nursing students' clinical decision-making competencies and artificial intelligence anxiety.

Population and Sample: The population of the research consists of senior students studying at Gülhane Faculty of Nursing, University of Health Sciences. Power analysis was performed to determine the number of people to be included in the study. The power of the test was calculated with the G*Power 3.1 program. In a similar study in the relevant literature, the effect size regarding the change in clinical decision-making level was calculated as 0.898. In order to exceed the 99% value in determining the power of the study; At the 5% significance level and 0.898 effect size, 44 people, 22 people in the groups, need to be reached (df=21; t=1.721). In the research, it was aimed to reach a total of 60 people, 30 people in the groups, considering the high power of the test and the losses.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Nurses, who are important members of the healthcare team, often encounter situations in the clinic that require them to make decisions. Nurses need to make a complex evaluation by using critical thinking and problem-solving skills in clinical decision-making.During nursing education, it is aimed at helping students acquire the skills to make this complex evaluation. To gain these skills, real-life experiences during clinical practices, case discussions, simulations, and skill laboratories can be used. It is thought that artificial intelligence applications, which have been increasingly used in recent years, can contribute to the development of clinical decision-making skills in nursing education. In this regard, this study aimed to investigate the effect of ChatGPT use training in the nursing process on nursing students' clinical decision-making competencies and artificial intelligence anxiety.

Study Type

Interventional

Enrollment (Actual)

60

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Turkey
    • Çankaya
      • Ankara, Çankaya, Turkey
        • Miray AKSU

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  1. Being a senior student at the faculty of nursing in the 2023-2024 academic year
  2. Agreeing to participate in the research

Exclusion Criteria:

  1. Having previously received ChatGPT training
  2. Withdrawing from the research voluntarily
  3. Incompletely filled forms

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Supportive Care
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Experimental
In the 2023-2024 academic year, those in the experimental group will be determined by randomization among the students who are senior students at the faculty of nursing, who have not received ChatGPT training before and who agree to participate in the research. Students in the experimental group will have data collection forms filled out. Then, 4 hours of ChatGPT based nursing process training will be given. Measurements will be taken again after the training and 6 weeks after the training.
Behavioral: ChatGPT Based Nursing Process Training

ChatGPT Based Nursing Process Training topics:

Lesson 1: Use of artificial intelligence and ChatGPT in healthcare Lesson 2: Using ChatGPT in determining nursing diagnoses and goals Lesson 3: Using ChatGPT to determine nursing interventions Lesson 4: Things to consider and ethical principles when using ChatGPT when creating a nursing process.
No Intervention: Control
In the 2023-2024 academic year, students who are senior students at the faculty of nursing, who have not received ChatGPT training before and who agree to participate in the research, will be determined by randomization and those in the control group will be determined. Students in the control group will fill out data collection forms. Students in the control group will not be given any training. Data collection forms will be applied again 6 weeks after the training.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Descriptive Information Form
Time Frame: 6 weeks
It was prepared by the researchers and consists of 6 questions regarding demographic characteristics and the use of artificial intelligence.
6 weeks
Clinical Decision Making Scale in Nursing
Time Frame: 6 weeks
The scale, which was developed to measure the clinical decision-making perceptions of nursing students based on their self-expression, was adapted into Turkish. The scale, which consists of four sub-dimensions, has 40 items. Sub-dimensions; It is stated as searching for options, declaring goals and values, evaluating and re-evaluating the results, searching for information and impartial assimilation of new information. Each subscale consists of 10 items. In the scale, 18 negative items are scored reversely. The five-point Likert type scale is scored as "Always-5, Frequently-4, Sometimes-3, Rarely-2 and Never-1" and the lowest and highest values are 40-200 for the total score of the scale and 40-200 for each sub-dimension. It varies between 10-50. High scores from the scale indicate that the perception of decision-making is high, while low scores indicate that the perception of decision-making is low.
6 weeks
Artificial Intelligence Anxiety Scale:
Time Frame: 6 weeks
Artificial Intelligence Anxiety Scale developed by Wang and Wang (2019) was adapted into Turkish by Akkaya et al (2021). The Turkish form of YZKS consists of 16 items and 4 dimensions and is a 5-point Likert type scale. The sub-dimensions are learning, job switching, sociotechnical blindness and artificial intelligence configuration.
6 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Saglik Bilimleri Universitesi

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 6, 2024

Primary Completion (Actual)

April 24, 2024

Study Completion (Actual)

August 14, 2024

Study Registration Dates

First Submitted

February 29, 2024

First Submitted That Met QC Criteria

February 29, 2024

First Posted (Actual)

March 7, 2024

Study Record Updates

Last Update Posted (Actual)

August 16, 2024

Last Update Submitted That Met QC Criteria

August 14, 2024

Last Verified

August 1, 2024

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • SBU-HEM-PSI-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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