Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets.

Yawen Guo, Rachael Zehrung, Katie Genuario, Xuan Lu, Qiaozhu Mei, Yunan Chen, Kai Zheng

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. 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.

Behavioral Innovations to Access Abortion Post-Dobbs: A Qualitative Thematic Analysis of Reddit's r/abortion Community in 2022.Inquiry : a journal of medical care organization, provision and financing
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

7 authors.

Yawen GuoUniversity of California, Irvine, Irvine, CA, USA.

Rachael ZehrungUniversity of California, Irvine, Irvine, CA, USA.

Katie GenuarioUniversity of California, Irvine, Irvine, CA, USA.

Xuan LuUniversity of Michigan, Ann Arbor, MI, USA.

Qiaozhu MeiUniversity of Michigan, Ann Arbor, MI, USA.

Yunan ChenUniversity of California, Irvine, Irvine, CA, USA.

Kai ZhengUniversity of California, Irvine, Irvine, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.

Indexed as

Abortion, LegalPrivacyConfidentialityFemaleHumansMachine LearningPregnancySupreme Court DecisionsUnited States

Identifiers

PMID38222378

PMCPMC10785943

What OpenQuestion holds

Textmetadata

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.