Evidence map›Paper›PMID 38250168›Full record

ArticleSocius : sociological research for a dynamic world

U.S. Immigrants Have Highly Heterogeneous Perceptions of How Selected They Are on Health.

Leafia Zi Ye, Alexis R Santos-Lozada

Open access · goldAbstract read
In one paragraph

Article in Socius : sociological research for a dynamic world. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 85% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Leafia Zi YeUniversity of Wisconsin-Madison, Madison, WI, USA.ORCID 0000-0001-7051-5518
Alexis R Santos-LozadaPennsylvania State University, University Park, PA, USA.ORCID 0000-0002-8437-4493
Pennsylvania State University · USUniversity of Wisconsin–Madison · US

Funding

Statistical Data Enclave CoreP30AG017266 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Michal Engelman · 1999 to 2026
$17.0M
The Population Research Institute: Development CoreP2CHD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI Brian Thiede · 2016 to 2026
$9.0M
Scientific and Technical CoreP2CHD047873 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI Katherine J. Curtis · 2014 to 2026
$5.1M
NIA NIH HHS P30 AG017266NICHD NIH HHS P2C HD041025NICHD NIH HHS P2C HD047873
6 · The paper itself

Abstract

Measuring immigrant health selection is crucial for understanding population health in immigrant-receiving countries. Recently, studies have measured health selection using respondents' perceptions of their health in comparison with those in their home countries. Yet we do not know how well this measure captures health selection. Using the New Immigrant Survey, the authors visualize respondents' self-reported levels of health selection stratified by self-rated health and by sending country. The visualization indicates that immigrants from the same country who rate their health the same still give very different answers when asked to compare their health with those in their home countries. These variations were observed for immigrants from all top five sending countries and at every level of self-reported health but are much larger among those who rate their health less favorably. Overall, the present findings signal that U.S. immigrants have highly heterogeneous perceptions of how selected they are.

Indexed as

health selectionmigrationself-rated healthUnited Statesvisualization

Identifiers

PMID38250168
PMCPMC10798264
OpenAlexW4297820338

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.