Evidence map›Paper›PMID 42212568›Full record

ArticleJournal of developmental and behavioral pediatrics : JDBP

Investigating the Quality of Chat Generative Pretrained Transformer's (ChatGPT) Parenting Advice.

Tiffany Munzer, Phoebe Jordan, Julie Sturza, Libby M Milkovich

Abstract read
In one paragraph

Article in Journal of developmental and behavioral pediatrics : JDBP. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Tiffany MunzerDepartment of Pediatrics, University of Michigan, Ann Arbor, MI.
Phoebe JordanDepartment of Pediatrics, Division of Psychology, University of Michigan, Ann Arbor, MI; and.
Julie SturzaDepartment of Pediatrics, University of Michigan, Ann Arbor, MI.
Libby M MilkovichDepartment of Pediatrics, Children's Mercy Kansas City, University of Missouri-Kansas City School of Medicine, Ann Arbor, MI.

Funding

Longitudinal associations of maternal mobile device use and maternal-infant wellbeingK23HD105988 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Tiffany Grace-Chung Munzer · 2022 to 2026
$837k
Eunice Kennedy Shriver National Institute of Child Health and Human Development K23HD105988NICHD NIH HHS K23 HD105988
6 · The paper itself

Abstract

objectivesChat Generative Pretrained Transformer (ChatGPT) is a widely adopted tool that can provide immediate parenting guidance. The aim of this study was to examine the quality of ChatGPT parenting advice.

methodsClinically relevant ChatGPT queries (n = 100) were created by investigators from a national poll on parents' most pressing concerns (screen media, mental health, feeding, school violence, smoking/vaping, and cost of health care/health insurance). ChatGPT 4o was queried for responses to these questions (June-July 2024). A coding scheme on parenting advice quality was adapted from previous literature with 15 items (presence or absence) and independently coded by an interdisciplinary team. Disagreements were resolved by majority consensus. Seven of these codes were summed, creating an overall quality variable (range 0-7, 7 = high). ANOVA with post hoc Tukey-Kramer analyses compared the overall quality of advice across topics (except cost of health care/health insurance). Analyses were conducted in SAS 9.4.

resultsChatGPT frequently shared accurate information about the benefits of parenting approaches and infrequently shared information about risks or psychosocial nuances such as developmental information, race/ethnic diversity, neurodiversity, and social drivers of health. There were differences in response quality between categories (F-value 4.29, p = 0.003). Post hoc analyses found that screen media advice (mean = 3.7, SD = 1.0) was of lower quality versus mental health (mean = 4.6, SD = 1.5, p = 0.03) and smoking/vaping (mean = 5.0, SD = 1.3, p = 0.03). Feeding mean was 3.7 (SD = 1.3); school violence mean was 4.8 (SD = 1.2); cost of health care/health insurance mean was 2.8 (SD = 0.6).

conclusionChatGPT's information was overall accurate; however, the quality of advice varied by parenting topic. ChatGPT quality may be lower for topics such as screen media, which may have more nuanced or variable approaches.

Indexed as

ParentingGenerative Artificial IntelligenceHumansartificial intelligencebehavioral healthChatGPTparenting

Identifiers

PMID42212568
PMCPMC13524179

What OpenQuestion holds

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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.