A qualitative analysis of stigmatizing language in birth admission clinical notes

Veronica Barcelona*, Danielle Scharp, Betina R. Idnay, Hans Moen, Dena Goffman, Kenrick Cato, Maxim Topaz

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
46 Downloads (Pure)

Abstract

The presence of stigmatizing language in the electronic health record (EHR) has been used to measure implicit biases that underlie health inequities. The purpose of this study was to identify the presence of stigmatizing language in the clinical notes of pregnant people during the birth admission. We conducted a qualitative analysis on N = 1117 birth admission EHR notes from two urban hospitals in 2017. We identified stigmatizing language categories, such as Disapproval (39.3%), Questioning patient credibility (37.7%), Difficult patient (21.3%), Stereotyping (1.6%), and Unilateral decisions (1.6%) in 61 notes (5.4%). We also defined a new stigmatizing language category indicating Power/privilege. This was present in 37 notes (3.3%) and signaled approval of social status, upholding a hierarchy of bias. The stigmatizing language was most frequently identified in birth admission triage notes (16%) and least frequently in social work initial assessments (13.7%). We found that clinicians from various disciplines recorded stigmatizing language in the medical records of birthing people. This language was used to question birthing people's credibility and convey disapproval of decision-making abilities for themselves or their newborns. We reported a Power/privilege language bias in the inconsistent documentation of traits considered favorable for patient outcomes (e.g., employment status). Future work on stigmatizing language may inform tailored interventions to improve perinatal outcomes for all birthing people and their families.

Original languageEnglish
Article numbere12557
JournalNursing Inquiry
Volume30
Issue number3
Early online date2023
DOIs
Publication statusPublished - Jul 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • bias
  • birth
  • discrimination
  • electronic health records
  • health disparities
  • pregnancy
  • qualitative research
  • social stigma

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