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Gender, Race, and Ethnicity of US Academic Ophthalmology Faculty and Department Chairs From 1966 to 2021

2024· letter· en· W4400534034 on OpenAlexaff
Brendan Tao, Jeffrey Ding, Edsel Ing, Radha P. Kohly, Robert Langan, Nawaaz Nathoo, Guillermo Rocha, Enitan Sogbesan, Salina Teja, Javed Siddiqi, Faisal Khosa

Bibliographic record

VenueJAMA Ophthalmology · 2024
Typeletter
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsVancouver General HospitalMcMaster UniversityUniversity of TorontoUniversity of AlbertaMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineEthnic groupWorkforceRace (biology)Underrepresented MinorityFamily medicineDiversity (politics)Health equityGerontologyCohortDemographyInternal medicinePublic healthMedical educationNursing

Abstract

fetched live from OpenAlex

Importance: Workforce diversity is integral to optimal function within health care teams. Objective: To analyze gender, race, and ethnicity trends in rank and leadership among US full-time academic ophthalmology faculty and department chairs between 1966 and 2021. Design, Setting, and Participants: This cohort study included full-time US academic ophthalmology faculty and department chairs registered in the Association of American Medical Colleges. Study data were analyzed in September 2023. Exposure: Identifying with an underrepresented in medicine (URiM) group. Main Outcomes and Measures: The main outcome measures were demographic (ie, gender, race, and ethnicity) changes among academic faculty and department chairs, assessed in 5-year intervals. The term minoritized race refers to any racial group other than White race. Results: There were 221 academic physicians in 1966 (27 women [12.2%]; 38 minoritized race [17.2%]; 8 Hispanic, Latino, or Spanish [3.6%]) and 3158 academic faculty by 2021 (1320 women [41.8%]; 1298 minoritized race [41.1%]; 147 Hispanic, Latino, or Spanish ethnicity [4.7%]). The annual proportional change for women, minoritized race, and Hispanic, Latino, or Spanish ethnicity was +0.63% per year (95% CI, 0.53%-0.72%), +0.54% per year (95% CI, 0.72%-0.36%), and -0.01% (95% CI, -0.03% to 0%), respectively. Women were underrepresented across academic ranks and increasingly so at higher echelons, ranging from nonprofessor/instructor roles (period-averaged mean difference [PA-MD], 19.88%; 95% CI, 16.82%-22.94%) to professor (PA-MD, 81.33%; 95% CI, 78.80%-83.86%). The corpus of department chairs grew from 77 in 1977 (0 women; 7 minoritized race [9.09%]; 2 Hispanic, Latino, or Spanish ethnicity [2.60%]) to 104 by 2021 (17 women [16.35%]; 22 minoritized race [21.15%]; 4 Hispanic, Latino, or Spanish ethnicity [3.85%]). For department chairs, the annual rate of change in the proportion of women, minoritized race, and Hispanic, Latino, or Spanish ethnicity was +0.32% per year (95% CI, 0.20%-0.44%), +0.34% per year (95% CI, 0.19%-0.49%), and +0.05% per year (95% CI, 0.02%-0.08%), respectively. In both faculty and department chairs, the proportion of URiM groups (American Indian or Alaska Native, Black or African American, Hispanic, and Native Hawaiian or Other Pacific Islander) grew the least. Intersectionality analysis suggested that men and non-URiM status were associated with greater representation across ophthalmology faculty and department chairs. However, among ophthalmology faculty, URiM women and men did not significantly differ across strata of academic ranks, whereas for department chairs, no difference was observed in representation between URiM men and non-URiM women. Conclusion & Relevance: Results of this cohort study revealed that since 1966, workforce diversity progressed slowly and was limited to lower academic ranks and leadership positions. Intersectionality of URiM status and gender persisted in representation trends. These findings suggest further advocacy and intervention are needed to increase workforce diversity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.362
Teacher spread0.282 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations8
Published2024
Admission routes1
Has abstractyes

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