Evidence map›Paper›PMID 29684190›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

Can online social support be detrimental in stigmatized chronic diseases? A quadratic model of the effects of informational and emotional support on self-care behavior of HIV patients.

Xunyi Wang, Srikanth Parameswaran, Darshan Mahendra Bagul, Rajiv Kishore

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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3 · Its place in the literature

Who cites it

9 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

4 authors.

Xunyi WangDepartment of Management Science and Systems, State University of New York at Buffalo, Buffalo, NY 14260-4000, USA.
Srikanth ParameswaranDepartment of Management Science and Systems, State University of New York at Buffalo, Buffalo, NY 14260-4000, USA.
Darshan Mahendra BagulDepartment of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260-4000, USA.
Rajiv KishoreDepartment of Management Science and Systems, State University of New York at Buffalo, Buffalo, NY 14260-4000, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: We studied the impact of online social support on patient self-care behavior in an online health community for human immunodeficiency virus (HIV) patients. We conceptualized emotional and informational support provided by community members into nuanced sub-dimensions. We explored how the direct and interaction effects of these sub-dimensions impact the self-care behavior of a support seeker. Methods: We used data from 330 255 posts in 30 050 threads from POZ, an online health community for HIV patients. Our key variables-self-care behaviori, objective informationj, experiential supportj, and emotional tonej-were operationalized using linguistic analysis with self-generated dictionaries and Python libraries. We tested our hypotheses using Tobit regression. Results: Out of 6 null hypotheses, 5 were rejected. Objective information and emotional tone had an inverted-U relationship with self-care behavior. Experiential information and community involvement were positively related to self-care behavior. Community involvement amplified the inverted-U relationship between emotional tone and self-care behavior. No significant interaction effect was found between experiential support and objective information. Conclusions: Beyond a threshold, both informational and emotional online social support had a deleterious impact on self-care behavior of HIV patients. Our results suggested that caution should be exercised in the use of online health community interventions for HIV patients, and perhaps patients with other stigmatized chronic diseases.

Indexed as

InternetSocial SupportChronic DiseaseConsumer Health InformaticsEmotionsHIV InfectionsHumansRegression AnalysisSelf CareStereotyping

Identifiers

PMID29684190
PMCPMC7646863

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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.