Evidence map›Paper›PMID 37902832›Full record

ArticleJournal of medical Internet research2023

Predicting and Empowering Health for Generation Z by Comparing Health Information Seeking and Digital Health Literacy: Cross-Sectional Questionnaire Study.

Wen Jiao, Angela Chang, Mary Ho, Qianfeng Lu, Matthew Tingchi Liu, Peter Johannes Schulz

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 3 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

24 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  5. [Development and validation of a health information-seeking behavior questionnaire for patients with non-communicable chronic diseases].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2026
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  18. Co-Designing a Framework for Social Media Health Communication to Young People: A Participatory Research Study.Health expectations : an international journal of public participation in health care and health policy · 2025
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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

6 authors.

Wen Jiao *School of Communication, Soochow University, Suzhou, China.ORCID 0000-0002-9270-0033
Angela Chang *Department of Communication, Faculty of Social Sciences, University of Macau, Macao, China.ORCID 0000-0003-3663-151X
Mary Ho *Faculty of Communication, Culture and Society, University of Lugano, Lugano, Switzerland.ORCID 0000-0002-5295-5349
Qianfeng Lu *Faculty of Communication, Culture and Society, University of Lugano, Lugano, Switzerland.ORCID 0000-0002-4825-0987
Matthew Tingchi LiuFaculty of Business and Administration, University of Macau, Macao, China.ORCID 0000-0002-1820-5691
Peter Johannes SchulzInstitute of Communication and Health, University of Lugano, Lugano, Switzerland.ORCID 0000-0003-4281-489X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGeneration Z (born 1995-2010) members are digital residents who use technology and the internet more frequently than any previous generation to learn about their health. They are increasingly moving away from conventional methods of seeking health information as technology advances quickly and becomes more widely available, resulting in a more digitalized health care system. Similar to all groups, Generation Z has specific health care requirements and preferences, and their use of technology influences how they look for health information. However, they have often been overlooked in scholarly research.

objectiveFirst, we aimed to identify the information-seeking preferences of older individuals and Generation Z (those between the ages of 18 and 26 years); second, we aimed to predict the effects of digital health literacy and health empowerment in both groups. We also aimed to identify factors that impact how both groups engage in digital health and remain in control of their own health.

methodsThe Health Information National Trends Survey was adopted for further use in 2022. We analyzed 1862 valid data points by conducting a survey among Chinese respondents to address the research gap. A descriptive analysis, 2-tailed t test, and multiple linear regression were applied to the results.

resultsWhen compared with previous generations, Generation Z respondents (995/1862, 53.44%) were more likely to use the internet to find out about health-related topics, whereas earlier generations relied more on traditional media and interpersonal contact. Web-based information-seeking behavior is predicted by digital health literacy (Generation Z: β=.192, P<.001; older population: β=.337, P<.001). While this was happening, only seeking health information from physicians positively predicted health empowerment (Generation Z: β=.070, P=.002; older population: β=.089, P<.001). Despite more frequent use of the internet to learn about their health, Generation Z showed lower levels of health empowerment and less desire to look for health information, overall.

conclusionsThis study examined and compared the health information-seeking behaviors of Generation Z and older individuals to improve their digital health literacy and health empowerment. The 2 groups demonstrated distinct preferences regarding their choice of information sources. Health empowerment and digital health literacy were both significantly related to information-seeking behaviors.

Indexed as

Health LiteracyTelemedicineAdolescentAdultCross-Sectional StudiesHumansInformation Seeking BehaviorInternetPower, PsychologicalSurveys and QuestionnairesYoung Adultdigital health literacydigitally savvyGeneration Zhealth empowermenthealth information seeking

Identifiers

PMID37902832
PMCPMC10644182

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.