Evidence map›Paper›PMID 30938756›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2019

Disposition toward privacy and information disclosure in the context of emerging health technologies.

Cynthia E Schairer, Cynthia Cheung, Caryn Kseniya Rubanovich, Mildred Cho, Lorrie Faith Cranor, Cinnamon S Bloss

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Special Section on Ethics in Health Informatics.Yearbook of medical informatics · 2020
    Review
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

6 authors.

Cynthia E SchairerDepartment of Psychiatry, School of Medicine, University of California, San Diego, San Diego, La Jolla, California, USA.
Cynthia CheungCenter for Wireless and Population Health Systems, California Institute for Telecommunications and Technology, University of California, San Diego, San Diego, La Jolla, California, USA.
Caryn Kseniya RubanovichClinical Psychology Joint Doctoral Program, San Diego State University/University of California, San Diego, San Diego, California, USA.
Mildred ChoDepartment of Pediatrics, Center for Biomedical Ethics, Stanford University, Stanford, California, USA.
Lorrie Faith CranorInstitute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Cinnamon S BlossDepartment of Psychiatry, School of Medicine, University of California, San Diego, San Diego, La Jolla, California, USA.

Funding

Impact of Privacy Environments for Personal Health Data on PatientsR01HG008753 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BLOSS, CINNAMON S · 2015 to 2017
$2.0M
NHGRI NIH HHS R01 HG008753
6 · The paper itself

Abstract

objectiveWe sought to present a model of privacy disposition and its development based on qualitative research on privacy considerations in the context of emerging health technologies. MATERIALS AND

methodsWe spoke to 108 participants across 44 interviews and 9 focus groups to understand the range of ways in which individuals value (or do not value) control over their health information. Transcripts of interviews and focus groups were systematically coded and analyzed in ATLAS.ti for privacy considerations expressed by respondents.

resultsThree key findings from the qualitative data suggest a model of privacy disposition. First, participants described privacy related behavior as both contextual and habitual. Second, there are motivations for and deterrents to sharing personal information that do not fit into the analytical categories of risks and benefits. Third, philosophies of privacy, often described as attitudes toward privacy, should be classified as a subtype of motivation or deterrent. DISCUSSION: This qualitative analysis suggests a simple but potentially powerful conceptual model of privacy disposition, or what makes a person more or less private. Components of privacy disposition are identifiable and measurable through self-report and therefore amenable to operationalization and further quantitative inquiry.

conclusionsWe propose this model as the basis for a psychometric instrument that can be used to identify types of privacy dispositions, with potential applications in research, clinical practice, system design, and policy.

Indexed as

AttitudeInformation DisseminationMedical Informatics ApplicationsPrivacyAdolescentAdultAgedAged, 80 and overConfidentialityDisclosureFemaleFocus GroupsHumansInterviews as TopicMaleMiddle Agedconfidentialitydisclosurepatient data privacyprivacysocial values

Identifiers

PMID30938756
PMCPMC6562158

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.