The Use of Non-Person-First Language in Consecutive General Ophthalmology Referrals
Bibliographic record
Abstract
PURPOSE: To investigate the prevalence of non-person-first language (PFL) in consecutive general ophthalmology referrals to a single tertiary ophthalmology clinic. DESIGN: Retrospective cross-sectional study. METHODS: Participants included Ophthalmology patients seen for their initial visit to a single tertiary ophthalmology clinic from July 2018 to December 2022. Ten randomly selected referrals from each day were screened for non-PFL as per the American Medical Association and American Psychological Association guidelines. Non-PFL was further categorized into general, diabetes, stigma, obesity, or ageism subcategories. The Chi-square test was used to evaluate associations between non-PFL use and referring provider gender and specialty, length of referral, and patient age and gender. RESULTS: A total of 2625 referrals were included in the study and 136 (5.2%) used non-PFL, such as referring to a person with diabetes as a "diabetic". Error types included Diabetes (38.2%), Stigma (30.9%), General (23.5%), Disability (8.8%), and Obesity (4.4%). Year of referral was predictive of non-PFL (P = .0016), with most occurring in 2020 (9.5%). Non-PFL was significantly more likely to occur in long length referrals compared to medium and short length referrals (16.2% vs. 5.1% vs. 3.5%, P < .001). Referring provider specialty was also predictive of non-PFL (P < .001) with most received by Family Medicine (8.3%), Optometry (4.4%), Emergency Medicine (0.62%), Ophthalmology (4.2%), Others (2.9%). Patient gender (P = .5563), patient age (P = .3466), and referring provider gender (P = .9057) were not predictive of non-PFL. CONCLUSIONS: Non-PFL use was most prevalent in 2020, with the most common referral sources being Family Medicine and Optometry. The highest proportions of non-PFL errors made were diabetes and stigma errors. Increased use of PFL in physician-physician communication can decrease intersectional stigma and promote inclusive patient care for ophthalmology patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".