Abstract
Background:
The research on ChatGPT-generated nursing care planning texts is critical for enhancing nursing education through innovative and accessible learning methods, improving reliability and quality.
Purpose:
The aim of the study was to examine the quality, authenticity, and reliability of the nursing care planning texts produced using ChatGPT.
Methods:
The study sample comprised 40 texts generated by ChatGPT selected nursing diagnoses that were included in NANDA 2021-2023. The texts were evaluated by using a descriptive criteria form and the DISCERN tool to evaluate health information.
Results:
DISCERN total average score of the texts was 45.93 ± 4.72. All texts had a moderate level of reliability and 97.5% of them provided moderate quality subscale score of information. A statistically significant relationship was found among the number of accessible references, reliability (r = 0.408), and quality subscale score (r = 0.379) of the texts (P < .05).
Conclusion:
ChatGPT-generated texts exhibited moderate reliability, quality of nursing care information, and overall quality despite low similarity rates.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Review and Exam Preparation,LPN and LVN,Fundamentals and skills,Education
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