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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".