Evidence map›Paper›PMID 35862307›Full record

ArticlePloS one2022

Think about your friends and family: The disparate impacts of relationship-centered messages on privacy concerns, protective health behavior, and vaccination against Covid-19.

Clara K Hanson, Kayuet Liu

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Clara K HansonDepartment of Sociology, University of California, Los Angeles, Los Angeles, California, United States of America.ORCID 0000-0002-7187-283X
Kayuet LiuDepartment of Sociology, University of California, Los Angeles, Los Angeles, California, United States of America.ORCID 0000-0002-8538-3711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo understand which factors affect how willing people are to share their personal information to combat the Covid-19 pandemic, and compare them to factors that affect other public health behaviors.

methodWe analyze data from three pre-registered online experiments conducted over eight months during the Covid-19 pandemic in the United States (April 3 2020 -November 25, 2020). Our primary analysis tests whether support for data sharing and intention to practice protective behavior increase in response to relationship-centered messages about prosociality, disease spread, and financial hardship. We then conduct a secondary correlational analysis to compare the demographic and attitudinal factors associated with willingness to share data, protective behavior, and intent to get vaccinated. Our sample (N = 650) is representative to socio-demographic characteristics of the U.S. population.

resultsWe find the altruistic condition increased respondents' willingness to share data. In our correlational analysis, we find interactive effects of political ID and socio-demographic traits on likelihood to share data. In contrast, we found health behavior was most strongly associated with political ID, and intent to vaccinate was more associated with socio-demographic traits.

conclusionsOur findings suggest that some public health messaging, even when it is not about data sharing or privacy, may increase public willingness to share data. We also find the role of socio-demographic factors in moderating the effect of political party ID varies by public health behavior.

Indexed as

COVID-19PrivacyFriendsHealth BehaviorHumansPandemicsUnited StatesVaccination

Identifiers

PMID35862307
PMCPMC9302763

What OpenQuestion holds

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LicenceCC BY
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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.