Gender inclusivity of ophthalmology journal submission guidelines and associated research metrics
Bibliographic record
Abstract
PURPOSE: This cross-sectional study evaluated the prevalence of inclusive author submission guidelines across ophthalmology journals. METHODS: Journals were identified from the 2021 Journal Citations Report (Clarivate Analytics). Independent reviewers rated each author submission guideline as "inclusive" for satisfying at-least one of six criteria: i) included examples of gender inclusive language; ii) recommended the use of gender-inclusive language; iii) distinguished between sex and gender; iv) provided educational resources on gender-inclusive language; v) provided a policy permitting name changes (e.g., in case of gender and name transition); and/or vi) provided a statement of commitment to inclusivity. The primary objective was to investigate the proportion of journals with "gender-inclusive" author submission guidelines and the elements of the gender-inclusive content within these guidelines. A secondary objective was to review the association between "gender-inclusivity" in author submission guidelines with publisher, origin country, and journal/source/influence metrics (Clarivate Analytics). RESULTS: Across 94 journals, 29.8% journals were rated as inclusive. Inclusive journals had significantly higher relative impact factor, citations, and article influence scores compared to non-inclusive journals. Of the 29.8% of inclusive journals, the three most common domains were inclusion of an inclusivity statement (71.4% of inclusive journals), distinguishing between sex and gender (67.9%), and provision of additional educational resources on gender reporting for authors (60.7%). CONCLUSION: A minority of ophthalmology journals have gender-inclusive author submission guidelines. Ophthalmology journals should update their submission guidelines to advance gender equity of both authors and study participants and promote the inclusion of gender-diverse communities.
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 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 | 0.052 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".