A Meta-Analysis of the Effectiveness of Guilt on Health-Related Attitudes and Intentions.
Clicks: 410
ID: 80788
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
70.7
/100
410 views
278 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #10 of 79 articles by views in Health Communication
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Guilt appeals are successful in encouraging healthy behaviors as proved by many studies. However, there has been no previous systematic review of guilt research in health domain. Thus, a meta-analysis of eight studies (2,061 subjects) was conducted to examine the effectiveness of guilt on health-related attitudes and intentions. The result revealed a strong positive overall effect of guilt (r = .49, 95% CI 0.31-0.64) despite the heterogeneity. Guilt had a stronger power in changing attitudes/intentions when paired with text-only messages than text-picture mixed messages. For studies using a college sample, the percentage of females marginally moderated the effect of guilt. Whether a message was self focused or other focused did not significantly moderate the effect of guilt. Future directions and practical implications are provided.
| Reference Key |
xu2018ahealth
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Xu, Zhan;Guo, Hao; |
| Journal | Health Communication |
| Year | 2018 |
| DOI |
10.1080/10410236.2017.1278633
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.