ArticleInternational journal of nursing sciences2025
Increasing breastfeeding literacy: A preliminary study to develop an AI-based chatbot.
Article in International journal of nursing sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Effects of Digital Health Interventions on Breastfeeding Rates, Self-Efficacy, and Knowledge: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Breastfeeding plays a critical role in the healthy development of infants, yet exclusive breastfeeding (EBF) rates remain low, particularly among low-income mothers. This study aimed to develop and validate an AI-based educational innovative solution to increase breastfeeding literacy across caregivers and mothers. Methods: The BabyChat (AI-based) was developed through two phases. In phase I, the content was created using the Canvas application, with the idea tree structured through MindMeister, and delivered via the ManyChat tool on Facebook. The focus was on the benefits of EBF during the initial 6 months of life, as recommended by the WHO, and continued breastfeeding until 1,000 days of life. In Phase II, functionality tests were performed using UserTesting and subsequently validated by the Content Validity Index (CVI). Healthcare professionals reviewed the clarity and relevance of the information on a four-point scale. Intra-examiner concordance was assessed by percentage of agreement and the median for each CVI-I point. Results: The contents of BabyChat included 8 topics and 18 subtopics (based on relevant contents including nutritional and anatomical aspects, weaning strategies among others) aimed to educate mothers and caregivers. Five mothers participated in evaluation of the BabyChat. Overall, most participants found the chatbot's question-and-answer functionality clear and helpful, with accurate command execution and timely response speeds, etc. However, two participants noted occasional issues such as misinterpreted questions, delayed command responses, and unclear or hard-to-find interface buttons. A total of four experts in psychology, dentistry, and medicine validated the framework. The agreement rate between experts ranged from 25 % to 100 %, with median values between 3 and 4, indicating excellent content relevance. Conclusion: The BabyChat was developed and validated for use in increasing breastfeeding literacy among caregivers and mothers. Future studies should be considered to expand the BabyChat validation to other healthcare professionals, including nursing staff, to comprehensively capture the impact of BabyChat on mothers, as well as to incorporate population-specific topics that depend on cultural and geographical aspects.
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Registered trials
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 Socratic graph.