Evidence map›Paper›PMID 41882482›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

Rewriting Doom: Serial Pathways from eHealth Use to Reduced Cancer Fatalism in Family Cancer History Groups.

Tianyi Liu, Jiazheng Zayn Wang, Xinshu Zhao

Abstract read
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In one paragraph

Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 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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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Tianyi Liu *Department of Communication/Institute of Collaborative Innovation/Center for Research in Greater Bay Area, University of Macau, Macau, China.
Jiazheng Zayn Wang *Faculty of Social Sciences, Department of Communication, University of Macau, Macau, China.ORCID http://orcid.org/0000-0002-3625-7751
Xinshu ZhaoDepartment of Communication/Institute of Collaborative Innovation/Center for Research in Greater Bay Area, University of Macau, Macau, China. xszhao@um.edu.mo.ORCID http://orcid.org/0000-0002-6038-0661

Funding

Universidade de Macau CRG2021-00002-ICI, ICI-RTO-0010-2021, CPG2023-00003-FSS
6 · The paper itself

Abstract

Cancer fatalism is a significant barrier to cancer prevention and treatment, especially among those with a family history of cancer. While eHealth information can influence cancer fatalism beliefs, the mechanisms behind this effect require further exploration. This study, using SOR theory, investigates whether eHealth information use can reduce cancer fatalism in individuals with a family history of cancer and elucidates the mechanisms involved. Data from the sixth National Trends in Health Information Survey (HINTS6) focused on participants with a family history of cancer (N = 3131). A serial-mediation model examined how eHealth information use affects cancer fatalism through mediators of cancer worry, frequency of doctor visits, and trust in cancer information from doctors. eHealth information use was negatively correlated with cancer fatalism. Additionally, cancer worry, frequency of doctor visits, and trust in cancer information from doctors were positively correlated with eHealth information use. Trust in cancer information from doctors was negatively correlated with cancer fatalism. These findings suggest that eHealth information, combined with interventions and offline communication with physicians, can effectively reduce cancer fatalism among individuals with a family history of cancer. Public health departments should leverage eHealth platforms to communicate accurate cancer information and foster trust in authoritative health sources like doctors, encouraging positive health behaviors and reducing cancer fatalism.

Indexed as

Cancer fatalismE-Health informationFamily cancer historySerial mediation

Identifiers

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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.