Reliability and Quality of the Nursing Care Planning Texts Generated by ChatGPT

DAĞCI M., ÇAM F., Dost A.

NURSE EDUCATOR, cilt.49, sa.3, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 49 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1097/nne.0000000000001566
  • Dergi Adı: NURSE EDUCATOR
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, CINAHL, EBSCO Education Source, Education Abstracts
  • İstanbul Üniversitesi Adresli: Hayır

Özet

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 descriptive criteria form and 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.