Gender-Inclusive Language in Public-Facing Labor and Delivery Web Pages in the New York Tristate Area: Cross-Sectional Study
Bibliographic record
Abstract
Background: Transgender and nonbinary (TGNB) individuals are increasingly intentionally becoming pregnant to raise children, and hospital websites should reflect these trends. For prospective TGNB parents, a hospital website is the only way they can assess their safety from discrimination while receiving perinatal care. Cisnormativity enforced by communication gaps between medical institutions and TGNB patients can and has caused delays in receiving urgent care during their pregnancy. Objective: The aim of this study was to evaluate the current prevalence of gender-inclusive terminology among labor and delivery services in the New York tristate area. Methods: The labor and delivery web pages of 189 hospitals from New York, New Jersey, and Connecticut were examined for gender-inclusive language. "Fully inclusive" websites explicitly acknowledged lesbian, gay, bisexual, transgender, queer, intersex, and asexual plus other gender- and sexual-oriented (LGBTQIA+) parents, "inclusive" websites did not use gendered terminology for parents, and "noninclusive" websites used gendered terms at least once in the text reviewed. The hospitals' web pages were further stratified by Healthcare Equality Index scores and population classifications defined by the 2013 National Center for Health Statistics Urban-Rural classification given to the county that each hospital was located in. Results: Of the 300 hospital websites reviewed, only 189 websites met the criteria for inclusion. Overall, only 6.3% (n=12) of labor and delivery web pages were "inclusive" or "fully inclusive." No geographic areas (P=.61) or Healthcare Equality Index scores (P=.81) were associated with inclusive or fully inclusive language. Conclusions: Hospitals need to use inclusive language to help TGNB people identify hospitals where their existence and needs are acknowledged and thus feel more comfortable in their transition to parenthood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".