ReviewJournal of general internal medicine2022
How to Reduce Stigma and Bias in Clinical Communication: a Narrative Review.
Review in Journal of general internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis assessing reliability of the Yale Food Addiction Scale: Implications for compulsive eating and obesity.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Hospital-Based Clinicians' Knowledge and Practices Regarding Stigmatizing Language in Clinical Documentation: A Multicenter Survey.Journal of general internal medicine · 2026Article
- From language matters to stigma-free communication in diabetes: Evolution, challenges, and next steps.Diabetic medicine : a journal of the British Diabetic Association · 2026Review
- The value of person-first language in forensic mental health services.BJPsych bulletin · 2026Article
- The Use of Biased Language in the Care of Seriously Ill Children: A Pilot Study.The American journal of hospice & palliative care · 2026Article
- Perceptions of terminology among individuals known to oncology services: a cross sectional survey.Journal of cancer survivorship : research and practice · 2026Article
- Experiences of Blame Among Pregnant Black Women During Prenatal Care.Journal of racial and ethnic health disparities · 2026Article
- How words discredit: A taxonomy of stigmatizing language in the electronic health record.Patient education and counseling · 2026Article
- Frontline clinicians' perceptions of the stigmatization of covid-19 and suggestions to prevent future health-related stigma: a qualitative study.BMC public health · 2026Article
- Oral health and mental health: lived experiences of stigma and discrimination for Australians with mental health conditions.BMC oral health · 2026Article
- Detecting stigmatizing language in clinical notes with large language models for addiction care.npj health systems · 2026Article
- Identifying Bias at Scale in Clinical Notes Using Large Language Models.Mayo Clinic proceedings. Digital health · 2025Article
- Donation initiators: Shifting the focus from outcome to first-time engagement - An interview study.Transfusion · 2025Article
- Beyond Keywords: A Qualitative Study of Context-Dependent Stigmatizing Language in Clinical Documentation from a Large, Multi-Payer Home Healthcare Agency.Home health care management & practice · 2025Article
- Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study.JMIR medical informatics · 2025Article
- Adherence Labeling: Understanding the Origins, Limitations, and Ethical Challenges of "Diagnosing" Nonadherence.Annals of family medicine · 2025Article
- Stigmatizing and Positive Language in Birth Clinical Notes Associated With Race and Ethnicity.JAMA network open · 2025Article
- Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Words matter: Destigmatizing the language of medicine through research, training, and future directions for emergency medicine.AEM education and training · 2025Review
- Health Care Practitioner Bias and Access to Inpatient Rehabilitation Services Among Survivors of Violence.JAMA network open · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A growing body of literature demonstrates that healthcare providers use stigmatizing language when speaking and writing about patients. In April 2021, the 21st Century Cures Act compelled clinicians to make medical records open to patients. We believe that this is a unique moment to provide clinicians with guidance on how to avoid stigma and bias in our language as part of larger efforts to promote health equity. We performed an exhaustive scoping review of the gray and academic literature on stigmatizing medical language. We used thematic analysis and concept mapping to organize the findings into core principles for use in clinical practice. We compiled a list of terms to avoid and seven strategies to promote non-judgmental health record keeping: (1) use person-first language, (2) eliminate pejorative terms, (3) make communication inclusive, (4) avoid labels, (5) stop weaponizing quotes, (6) avoid blaming patients, and (7) abandon the practice of leading with social identifiers. While we offer guidance clinicians can use to promote equity through language on an individual level, health inequities are structural and demand simultaneous systems and policy change. By improving our language, we can disrupt the harmful narratives that allow health disparities to persist.
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What OpenQuestion holds
Registered trials
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.