Evidence map›Paper›PMID 41726487›Full record

SynthesisAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Understanding Stigmatizing Language Lexicons: A Comparative Analysis in Clinical Contexts.

Yiliang Zhou, Di Hu, Tianchu Lyu, Jasmine Dhillon, Alexandra L Beck, Gelareh Sadigh, Kai Zheng

Abstract readComparative StudySystematic Review
In one paragraph

Synthesis in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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
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.

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

7 authors.

Yiliang ZhouUniversity of California, Irvine, Irvine, CA, United States.
Di HuUniversity of California, Irvine, Irvine, CA, United States.
Tianchu LyuUniversity of California, Irvine, Irvine, CA, United States.
Jasmine DhillonUniversity of California, Irvine, Irvine, CA, United States.
Alexandra L BeckUniversity of California, Irvine, Irvine, CA, United States.
Gelareh SadighUniversity of California, Irvine, Irvine, CA, United States.
Kai ZhengUniversity of California, Irvine, Irvine, CA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stigmatizing language results in healthcare inequities, yet there is no universally accepted or standardized lexicon defining which words, terms, or phrases constitute stigmatizing language in healthcare. We conducted a systematic search of the literature to identify existing stigmatizing language lexicons and then analyzed them comparatively to examine: 1) similarities and discrepancies between these lexicons, and 2) the distribution of positive, negative, or neutral terms based on an established sentiment dataset. Our search identified four lexicons. The analysis results revealed moderate semantic similarity among them, and that most stigmatizing terms are related to judgmental expressions by clinicians to describe perceived negative behaviors. Sentiment analysis showed a predominant proportion of negatively classified terms, though variations exist across lexicons. Our findings underscore the need for a standardized lexicon and highlight challenges in defining stigmatizing language in clinical texts.

Indexed as

Natural Language ProcessingSocial StigmaStereotypingTerminology as TopicElectronic Health RecordsHumansSemanticselectronic health recordhealth equitynatural language processingstigmastigmatizing language

Identifiers

PMID41726487
PMCPMC12919506

What OpenQuestion holds

Textmetadata
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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.