Evidence map›Paper›PMID 42253841›Full record

ArticleInternational journal of women's health2026

Online Health Information Seeking for Pregnancy-Related Resources and Maternal eHealth Literacy Among Women in Thailand: A Qualitative Study.

Amornrat Deeboonno, Wantana Maneesriwongul, Srisamorn Phumonsakul, Pisamai Orathai

Abstract read
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Article in International journal of women's health, 2026. 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

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.

2 · The registry

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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.

Amornrat DeeboonnoRamathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.ORCID 0009-0004-4519-1178
Wantana ManeesriwongulRamathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.ORCID 0000-0002-7436-1533
Srisamorn PhumonsakulRamathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.ORCID 0000-0003-3105-6667
Pisamai OrathaiRamathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.ORCID 0000-0003-1467-9504

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Maternal eHealth literacy (MeHL) is a critical factor influencing self-care and health promotion during pregnancy. Inadequate MeHL may hinder pregnant women's effective use of online maternal health resources, increasing vulnerability to misinformation and suboptimal health decisions. However, research specifically exploring MeHL among pregnant women remains limited. Prior studies have largely focused on general populations or employed quantitative approaches, providing limited insight into how pregnant women navigate and make sense of online maternal health information in practice. Qualitative exploration is therefore needed to capture the contextual and multidimensional nature of these experiences. Objective: To explore how pregnant and postpartum women in Thailand seek, appraise, and apply online health information during pregnancy. Methods: This qualitative phase of a mixed-methods study aimed to develop an instrument for assessing electronic health literacy (eHL) among Thai pregnant women. Using a descriptive qualitative design guided by the eHealth Literacy Framework (eHLF), we explored pregnant women's internet use and eHL related experiences through in-depth interviews conducted between January and February 2024 with a purposive sample of 12 pregnant and 8 postpartum women from urban and suburban hospitals in Thailand. Results: Participants primarily used Google, YouTube, and Facebook for pregnancy information. eHLF-guided analysis identified themes across all seven eHL domains. Although participants demonstrated skills in accessing information, they expressed concerns regarding source credibility, data privacy, and complex medical terminology, and highlighted the need for Thai-language, user-friendly digital resources provided or endorsed by trusted national healthcare institutions. Conclusion: Pregnant women in these Thai hospital-based samples are active users of eHealth information but face challenges in navigating credibility and system usability. Healthcare providers and institutions should develop and promote reliable, accessible, and tailored digital health resources to enhance MeHL.

Indexed as

eHealth literacymaternal eHealth literacymaternal healthpregnancy informationpregnant women

Identifiers

PMID42253841
PMCPMC13242205

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LicenceCC BY-NC
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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.