ArticleCureus2023
Artificial Intelligence in Childcare: Assessing the Performance and Acceptance of ChatGPT Responses.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- University students' acceptance of generative artificial intelligence tools: a mixed-methods study on opinions, attitudes, and behavioral intentions.BMC psychology · 2026Article
- A framework for human evaluation of large language models in healthcare derived from literature review.NPJ digital medicine · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose This study aimed to evaluate the performance and acceptance of responses generated by ChatGPT-3.5 and GPT-4 to Japanese childcare-related questions to assess their potential applicability and limitations in the childcare field, specifically focusing on the accuracy, usefulness, and empathy of the generated answers. Methods We evaluated answers in Japanese generated by GPT-3.5 and GPT-4 for two types of childcare-related questions. ① For the written examination questions of Japan's childcare worker national examination for 2023's fiscal year, we calculated the correct answer rates using official answers. ② We selected one question from each of the seven categories from the child-rearing questions posted on the Japanese National Childcare Workers Association's website and had GPT-3.5 and GPT-4 generate answers. These were evaluated alongside existing childcare worker answers by human professionals. Five childcare workers then blindly selected what they considered the best answer among the three and rated them on a five-point scale for 'accuracy,' 'usefulness,' and 'empathy.' Results In the examination consisting of 160 written questions, both GPT-3.5 and GPT-4 produced responses to all 155 questions, excluding four questions omitted due to copyright concerns and one question deemed invalid due to inherent flaws in the question itself, with correct answer rates of 30.3% for GPT-3.5 and 47.7% for GPT-4 (p<0.01). For the child-rearing Q&A questions, childcare worker answers by human professionals were chosen as the best answer most frequently (45.7%), followed by GPT-3.5 (31.4%) and GPT-4 (22.9%). While GPT-3.5 received the highest average rating for accuracy (3.69 points), childcare worker answers by human professionals received the highest average ratings for usefulness and empathy (both 3.57 points). Conclusions Both GPT-3.5 and GPT-4 failed to meet the passing criteria in Japan's childcare worker national examination, and for the child-rearing questions, GPT-3.5 was rated higher in accuracy despite lower correct answer rates. Over half of the childcare workers considered the ChatGPT-generated answers to be the best ones, yet concerns about accuracy were observed, highlighting the potential risk of incorrect information in the Japanese context.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.