Evidence map›Paper›PMID 40001834›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Psychometric Analysis of Items Evaluating Health Belief Model Constructs in Social Media Posts: Application of Rasch Measurement Model.

Xiaofeng Jia, Soyeon Ahn

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

2 authors.

Xiaofeng JiaSchool of Media & Communication, Bowling Green State University, Bowling Green, OH 43403, USA.ORCID 0000-0003-1709-2776
Soyeon AhnDepartment of Educational and Psychological Studies, School of Education and Human Development, University of Miami, Coral Gables, FL 33146, USA.ORCID 0000-0003-2581-306X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social media is a crucial tool for health communication as it provides an immediate, wide-reaching platform to share information, correct misinformation, and promote health behaviors. The Health Belief Model (HBM) offers a structured approach for designing more effective social media messages by employing unique constructs predicting health behaviors, such as severity, susceptibility, benefits, barriers, and self-efficacy. While prior research has explored HBM constructs in health messages, most studies have collected the survey data with items lacking robust psychometric evidence, particularly in evaluating social media posts. This study addresses this gap by using Rasch Measurement Theory (RMT) to analyze the psychometric properties of HBM items evaluating social media posts promoting COVID-19 vaccination. The findings indicate that severity, benefits, and barriers are the most reliable HBM constructs in social media posts, while susceptibility and self-efficacy are underutilized in health messaging for social media. Also, dimensionality analysis confirms distinct patterns, but unexplained variance suggests that additional factors influence vaccine messaging, raising validity concerns. These results underscore the need to refine HBM-based message strategies by emphasizing overlooked constructs and improving item effectiveness. This study provides guidelines for using HBM-related measures in social media by establishing comprehensive psychometric properties, especially when applied in social media contexts. It also presents practical guidelines for designing and evaluating social media health messages, ensuring they effectively utilize HBM constructs to promote positive health behaviors. Future research should explore measurement invariance and content creators' emphasis on HBM constructs, leveraging high-engagement tweets while expanding to diverse perspectives for broader applicability.

Indexed as

health belief modelhealth promotionpsychometric analysisRasch measurement modelsocial media messages

Identifiers

PMID40001834
PMCPMC11851386

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.