Characterizing Stigmatizing and Biased Language in Clinical Pharmacist Documentation
Bibliographic record
Abstract
ABSTRACT Introduction Biased language in documentation can perpetuate stigma, influence treatment decisions, and impact provider–patient relationships. As any person seeking care at acute care hospitals may face stigma, particularly those with substance use or mental health disorders, unbiased documentation is crucial. We sought to determine the prevalence of stigmatizing and biased language in electronic health records written by clinical pharmacists. Methods This study was conducted at two acute care teaching hospitals, St. Paul's and Mount Saint Joseph Hospitals in Vancouver, British Columbia, Canada. A list of stigmatizing and biased terms was compiled through literature review and expert consensus. A retrospective, observational, cross‐sectional study of clinical pharmacist notes was performed using a data‐mining algorithm to identify these terms. A content analysis was conducted to explore the ways this terminology was used and to uncover new themes not previously documented in the literature. Results Between November 16, 2019, and September 30, 2023, of 135 671 clinical pharmacist notes reviewed, 42 192 (31.1%) contained at least one stigmatizing or biased term. Commonly identified terms included: compliance, noncompliance, refuses, denies, and smoker. All themes previously documented in the literature (e.g., leading with race/socioeconomic status, incorrect pronouns, employing quotations to suggest lack of credibility) were observed. Additionally, new themes emerged, including the use of punctuation or formatting to amplify the stigmatizing tone and the role of electronic health records in perpetuating stigma. Discussion Stigmatizing language was found in 31.1% of clinical pharmacist notes. Findings from this study are assisting in the development of a multimodal educational intervention aimed at reducing the prevalence of stigmatizing language in clinical pharmacist documentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".