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Record W4401200944 · doi:10.2196/53057

Gender-Inclusive Language in Public-Facing Labor and Delivery Web Pages in the New York Tristate Area: Cross-Sectional Study

2024· article· en· W4401200944 on OpenAlexvenueno aff
S. Isaac, Mark Dawes, E. Howell, Antonia Francis Oladipo

Bibliographic record

VenueJMIR Human Factors · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintCross-sectional studyPolitical scienceSociologyWorld Wide WebComputer scienceMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.100
GPT teacher head0.414
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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