Evidence map›Paper›PMID 40391229›Full record

ArticleAsia-Pacific journal of oncology nursing2025

Patterns and associated factors of online health information seeking behaviors among young women diagnosed with breast cancer in China.

Jialin Chen, Yang Yang, Haozhi Xia, Yiwen Duan, Chaojin Da, Tingting Cai, Changrong Yuan

Abstract read
In one paragraph

Article in Asia-Pacific journal of oncology nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

Who cites it

6 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

7 authors.

Jialin ChenSchool of Nursing, Fudan University, Shanghai, China.
Yang YangDepartment of Nursing, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Haozhi XiaSchool of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.
Yiwen DuanSchool of Nursing, Fudan University, Shanghai, China.
Chaojin DaDepartment of Nursing, School of Clinical Nursing, Gansu Health Vocational College, Lanzhou, China.
Tingting CaiSchool of Nursing, Fudan University, Shanghai, China.
Changrong YuanSchool of Nursing, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to identify latent classes of online health information seeking (OHIS) behaviors among young women diagnosed with breast cancer in China and examine associated personal characteristics to support tailored health education strategies. Methods: Young women diagnosed with breast cancer were recruited from a cancer center in China between April and September 2024. Participants completed questionnaires on demographic and clinical characteristics, OHIS behaviors, psychosocial and cognitive factors, trust, social norms, communication, and information seeking experience. Latent class analysis (LCA) identified OHIS patterns, and multivariate logistic regression explored associated characteristics. Results: Among the 398 patients, the median number of topics sought was 5 (4-7). The most frequently sought topics related to breast cancer included basic knowledge (89.7%), treatment plans (77.6%), and lifestyle (75.4%). Nearly half sought information only a few times a month or less. Social media (82.7%) and official accounts/websites (71.1%) were the most frequently used sources. LCA revealed three OHIS behavior classes: Class 1 "information explorers" (26.4%), Class 2 "occasional seekers" (49.2%), and Class 3 "information experts" (24.4%). Patients in adjuvant or other treatment phases were more likely to belong to Class 2 than Class 1. Those with a longer time since diagnosis were also more likely to be classified into Class 2 or Class 3. Conversely, stage I patients and those who trusted online health information were more likely to belong to Class 1, while higher eHealth literacy was associated with Class 3 membership. Conclusions: Young women diagnosed with breast cancer display diverse OHIS patterns influenced by demographic and clinical factors. Recognizing these differences is vital for delivering tailored online health information services.

Indexed as

Breast cancerHealth behaviorLatent class analysisOnline health information seeking behaviors

Identifiers

PMID40391229
PMCPMC12088742

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.