Evidence map›Paper›PMID 19916635›Full record

ArticleHealth psychology : official journal of the Division of Health Psychology, American Psychological Association2009

Opt-out testing for stigmatized diseases: a social psychological approach to understanding the potential effect of recommendations for routine HIV testing.

Sean D Young, Benoit Monin, Douglas Owens

Abstract read
In one paragraph

Article in Health psychology : official journal of the Division of Health Psychology, American Psychological Association, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 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

3 authors.

Sean D YoungDivision of Infectious Disease/Program in Global Health, epartment of Medicine, University of California, Los Angeles, CA 90095, USA. sdyoung@mednet.ucla.edu
Benoit Monin
Douglas Owens

Funding

Postdoctoral Training in Global AIDS Prevention ResearchT32MH080634 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JESSE LAWTON CLARK, Pamina Mae Gorbach · 2007 to 2026
$6.4M
NIMH NIH HHS T32 MH080634
6 · The paper itself

Abstract

objectiveLittle research has studied experimentally whether an opt-out policy will increase testing rates or whether this strategy is especially effective in the case of stigmatized diseases such as HIV. DESIGN AND

main outcome measuresIn Study 1, a 2 x 2 factorial design asked participants to make moral judgments about a person's decision to test for stigmatized diseases under an opt-in versus an opt-out policy. In Study 2, a 2 x 2 factorial design measuring testing rates explored whether opt-out methods reduce stigma and increase testing for stigmatized diseases.

resultsStudy 1 results suggest that getting tested draws suspicion regarding moral conduct in an opt-in system, whereas not getting tested draws suspicion in an opt-out system. Study 2 results suggest that an opt-out policy may increase testing rates for stigmatized diseases and lessen the effects of stigma in people's reluctance to test. DISCUSSION: A social psychological approach to health services can be used to show how testing policies can influence both the stigmatization associated with testing and participation rates. An understanding of how testing policies may affect patient decision making and behavior is imperative for creating effective testing policies.

Indexed as

StereotypingAdolescentAdultCenters for Disease Control and Prevention, U.S.Decision MakingFemaleHIV SeropositivityHumansJudgmentMaleMass ScreeningOrganizational PolicyPatient Acceptance of Health CareUnited StatesYoung Adult

Identifiers

PMID19916635
PMCPMC2965185

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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.