ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
Use of Health Belief Model-based Deep Learning to Understand Public Health Beliefs in Breast Cancer Screening from Social Media before and during the COVID-19 Pandemic.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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
5 citing papers in PubMed.
- Enhancing cancer prevention behaviors through social media: the role of source credibility and message characteristics in Ghana.BMC public health · 2025Article
- The Chain Mediation Role of Self-Efficacy, Health Literacy, and Physical Exercise in the Relationship Between Internet Use and Older Adults' Health: Cross-Sectional Questionnaire Study.Journal of medical Internet research · 2025Article
- Psychometric validation of a culturally adapted health belief model scale for breast cancer screening in Chinese women.PloS one · 2025Article
- Evaluation of the multifaceted role of social media in cancer patient care.Ecancermedicalscience · 2025Article
- The predictors of voluntary participation in pulmonary tuberculosis screening program: a study in a suburban community of southern Thailand.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the second leading cause of cancer death for women in the United States. While breast cancer screening participation is the most effective method for early detection, screening rate has remained low. Given that understanding health perception is critical to understand health decisions, our study utilized the Health Belief Model-based deep learning method to predict and examine public health beliefs in breast cancer and its screening behavior. The results showed that the trends in public health perception are sensitive to political (i.e., changes in health policy), sociological (i.e., representation of disease and its preventive care by public figure or organization), psychological (i.e., social support), and environmental factors (i.e., COVID-19 pandemic). Our study explores the roles social media can play in public health surveillance and in public health promotion of preventive care.
Indexed as
Identifiers
38222395PMC10785880What OpenQuestion holds
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 OpenQuestion graph.