Evidence map›Paper›PMID 37791148›Full record

ArticleCureus2023

Artificial Intelligence in Childcare: Assessing the Performance and Acceptance of ChatGPT Responses.

Yudai Kaneda, Mira Namba, Uiri Kaneda, Tetsuya Tanimoto

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yudai KanedaSchool of Medicine, Hokkaido University, Sapporo, JPN.
Mira NambaSchool of Medicine, Keio University, Tokyo, JPN.
Uiri KanedaFaculty of Foreign Languages, Dokkyo University, Soka, JPN.
Tetsuya TanimotoInternal Medicine, Jyoban Hospital of Tokiwa Foundation, Iwaki, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ai & robotics healthcarechatgptchildcarejapanperformance and acceptance

Identifiers

PMID37791148
PMCPMC10544433

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.