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Record W4412071859 · doi:10.3390/healthcare13131630

Advancing Gender Equity in International Eyecare: A Roadmap in Creating the Women Leaders in Eye Health (WLEH) Initiative

2025· article· en· W4412071859 on OpenAlexaff
Clare Szalay Timbo, Armaan Jaffer, María José Romero, Gabriela Cubias, Heidi Chase, Sara T. Wester, Femida Kherani, Erin M. Shriver

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsGender equityEquity (law)Health equityPolitical sciencePublic relationsPsychologyEconomic growthBusinessGender studiesSociologyEconomicsHealth careLaw

Abstract

fetched live from OpenAlex

Gender inequality remains a persistent issue in healthcare, especially in ophthalmology, where women face systemic barriers such as pay gaps, limited surgical opportunities, harassment, and unequal family expectations. Despite increasing entry into the field, women remain underrepresented in leadership, affecting career advancement and patient care. This study examines how virtual platforms, and co-led initiatives can address gender disparities in eye health. In 2021, Women in Ophthalmology, Seva Foundation, and Orbis International launched the Women's Leaders in Eye Health (WLEH) initiative-a global community promoting mentorship, networking, and leadership development. Starting with virtual webinars and informal networking, the initiative expanded to in-person events in 2023 due to strong global engagement and demand. Early virtual programming, including webinars and "Coffee Hour" sessions, proved effective and laid the groundwork for broader offerings such as mentorship and professional development grants. WLEH's success underscores the power of collaboration in promoting gender equity. By fostering connections and leadership pathways, WLEH offers a scalable model to break down gender challenges and uplift the next generation of women leaders to deliver more accessible eyecare globally.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.463
Teacher spread0.351 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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