Evidence map›Paper›PMID 42830366›Full record

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

Understanding Cancer Information Burden and Quality Concerns: The Roles of Medical Statistics Literacy, Online Health Information Confidence, and Social Media.

Aditya Chakraborty, Sikta Das, Amy Myat Phyo

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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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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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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Aditya ChakrabortyDepartment of Epidemiology, Biostatistics, and Environmental health, Joint School of Public Health, Old Dominion University, Norfolk, VA, USA. chakraa@odu.edu.ORCID http://orcid.org/0000-0002-1476-113X
Sikta DasDepartment of Economics, University of South Florida, Tampa, FL, USA.
Amy Myat PhyoDepartment of Industrial Engineering, University of Central Florida, Orlando, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding factors shaping perceptions of cancer-related information is important for public health communication. This study examined associations of three information-related predictors, understanding medical statistics, confidence in online health information, and difficulty interpreting social media information, with two outcomes: cancer information was too hard to understand (CTU) and concern about cancer information quality (CCQ). Data came from the 2022 Health Information National Trends Survey. The cross-sectional study included 1972 participants. Survey-weighted univariable and multivariable logistic regression models assessed associations between the three predictors and CTU and CCQ, adjusting for sociodemographic characteristics, health insurance, and cancer history. LASSO logistic regression, random forest, XGBoost, and support vector models were implemented to assess prediction performances for the outcomes. SHAP and variable importance plots identified influential predictors. Difficulty understanding medical statistics was associated with higher odds of agreeing CTU (AOR = 1.89, 95% CI: 1.17-3.05) and agreeing CCQ (AOR = 1.69, 95% CI: 1.11-2.57). Lower confidence in finding helpful health information online was associated with higher odds of both outcomes, whereas complete confidence was associated with lower odds relative to very confident. Greater difficulty interpreting social media information was associated with CTU (AOR = 2.92, 95% CI: 1.66-5.14) and CCQ (AOR = 1.89, 95% CI: 1.07-3.33). SHAP and variable importance identified difficulty understanding medical statistics, confidence in online health information, and difficulty interpreting health information on social media as key predictors. These findings emphasize public health communication strategies prioritizing statistical literacy, clearer cancer information, and improved evaluation of online and social media health content.

Indexed as

Cancer information seekingHealth information complexityHealth literacyMedical statistics literacyPerceived information burdenPredictive modelsSHAP

Identifiers

PMID42830366

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