Evidence map›Paper›PMID 31518243›Full record

ArticleJMIR aging2018

Understanding Why Older Adults With Type 2 Diabetes Join Diabetes Online Communities: Semantic Network Analyses.

Jakeem Amir Lewis, Perry M Gee, Chia-Ling Lynn Ho, Lisa M Soederberg Miller

Abstract read
In one paragraph

Article in JMIR aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 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.

Jakeem Amir Lewis *Department of Human Ecology, University of California, Davis, Davis, CA, United States.ORCID http://orcid.org/0000-0003-0534-5481
Perry M GeeNursing Research and Analytics, Dignity Health, San Francisco, CA, United States.ORCID http://orcid.org/0000-0002-3107-2715
Chia-Ling Lynn HoDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID http://orcid.org/0000-0001-5916-8260
Lisa M Soederberg MillerDepartment of Human Ecology, University of California, Davis, Davis, CA, United States.ORCID http://orcid.org/0000-0001-8261-1814

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs individuals age, chronic health difficulties may disrupt physical and social well-being. Individuals can turn to online communities to interact with similar peers, which may help buffer negative effects resulting from health difficulties.

objectiveThis study investigated the reasons that older adults join a diabetes online community to better understand the specific resources that are being sought.

methodsWe used semantic network analyses to categorize the reasons participants provided for joining a community during the sign-up process.

resultsThe most frequent reasons for joining were to seek information about their health condition, to help with self-management of health difficulties, for feelings of informational and social support, and for having a community with whom to share. Women were more likely to go online for sharing and companionship as well as for information and social support reasons, whereas men were more likely to go online for general information and self-management reasons.

conclusionsThis study shows the reasons older adults seek to join a diabetes online community: for increased information and support regarding chronic health difficulties. Practitioners may want to consider ways to promote access to online communities among their older patients as a source of health information and a resource to provide a sense of community.

Indexed as

chronic health difficultydiabeteshealth informationhealth supportonline communityself-managementsocial support

Identifiers

PMID31518243
PMCPMC6715014

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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.