Evidence map›Paper›PMID 38931294›Full record

ArticleNutrients2024

Assessing the Quality of ChatGPT's Dietary Advice for College Students from Dietitians' Perspectives.

Li-Ling Liao, Li-Chun Chang, I-Ju Lai

Abstract read
In one paragraph

Article in Nutrients, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–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

18 citing papers in PubMed.

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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

3 authors.

Li-Ling LiaoDepartment of Public Health, College of Health Science, Kaohsiung Medical University, Kaohsiung City 807378, Taiwan.ORCID 0000-0003-0867-2699
Li-Chun ChangSchool of Nursing, Chang Gung University of Science and Technology, Tao-Yuan 333324, Taiwan.ORCID 0000-0002-6704-8995
I-Ju LaiDepartment of Nutrition, I-Shou University, Kaohsiung City 824005, Taiwan.ORCID 0000-0001-9089-9408

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs ChatGPT becomes a primary information source for college students, its performance in providing dietary advice is under scrutiny. This study assessed ChatGPT's performance in providing nutritional guidance to college students.

methodsChatGPT's performance on dietary advice was evaluated by 30 experienced dietitians and assessed using an objective nutrition literacy (NL) test. The dietitians were recruited to assess the quality of ChatGPT's dietary advice, including its NL achievement and response quality.

resultsThe results indicate that ChatGPT's performance varies across scenarios and is suboptimal for achieving NL with full achievement rates from 7.50% to 37.56%. While the responses excelled in readability, they lacked understandability, practicality, and completeness. In the NL test, ChatGPT showed an 84.38% accuracy rate, surpassing the NL level of Taiwanese college students. The top concern among the dietitians, cited 52 times in 242 feedback entries, was that the "response information lacks thoroughness or rigor, leading to misunderstandings or misuse". Despite the potential of ChatGPT as a supplementary educational tool, significant gaps must be addressed, especially in detailed dietary inquiries.

conclusionThis study highlights the need for improved AI educational approaches and suggests the potential for developing ChatGPT teaching guides or usage instructions to train college students and support dietitians.

Indexed as

NutritionistsStudentsAdultDietFemaleHealth Knowledge, Attitudes, PracticeHealth LiteracyHumansMaleTaiwanUniversitiesYoung AdultChatGPTdietary dvicehealth educationnutrition literacy

Identifiers

PMID38931294
PMCPMC11206595

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.