Evidence map›Paper›PMID 35654433›Full record

ArticleYearbook of medical informatics2022

Social Media, Digital Health Literacy, and Digital Ethics in the Light of Health Equity.

Octavio Rivera-Romero, Elia Gabarron, Talya Miron-Shatz, Carolyn Petersen, Kerstin Denecke

Abstract read
In one paragraph

Article in Yearbook of medical informatics, 2022. 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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  6. Community engagement in the development of health-related data visualizations: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024
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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

5 authors.

Octavio Rivera-RomeroInstituto de Ingeniería Informática (I3US), Universidad de Sevilla, Sevilla, Spain.
Elia GabarronNorwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway.
Talya Miron-ShatzOno Academic College, Israel.
Carolyn PetersenMayo Clinic, United States.
Kerstin DeneckeInstitute for Medical Informatics, Bern University of Applied Sciences, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSocial media is used in the context of healthcare, for example in interventions for promoting health. Since social media are easily accessible they have potential to promote health equity. This paper studies relevant factors impacting on health equity considered in social media interventions.

methodsWe searched for literature to identify potential relevant factors impacting on health equity considered in social media interventions. We included studies that reported examples of health interventions using social media, focused on health equity, and analyzed health equity factors of social media. We identified Information about health equity factors and targeted groups.

resultsWe found 17 relevant articles. Factors impacting on health equity reported in the included papers were extracted and grouped into three categories: digital health literacy, digital ethics, and acceptability.

conclusionsLiterature shows that it is likely that digital technologies will increase health inequities associated with increased age, lower level of educational attainment, and lower socio-economic status. To address this challenge development of social media interventions should consider participatory design principles, visualization, and theories of social sciences.

Indexed as

Health EquityHealth LiteracySocial MediaHealth PromotionHumans

Identifiers

PMID35654433
PMCPMC9719782

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.