Abstracts from the Student Medical Summit 2023
Bibliographic record
Abstract
BackgroundFemale representation in obstetrics and gynecology (ob/gyn) meetings is important for gender equity.Our objective was to examine if a gender bias exists and to determine whether male or female faculty of equal academic merit were offered equal opportunities to speak at major North American ob/gyn conferences. Materials and methodsConferences and presenter information were electronically retrieved.Conference information extracted involved: name, organizing institution, number of sections, and duration.Presenter information was extracted in duplicate and independently.Presenter information extracted involved: name, speaking time, h-index, number of publications, number of citations and years of practice.Gender was determined with online software Genderize.io.Conflicts were resolved with a third reviewer.One-way ANOVA was conducted to evaluate the difference in parametric variables and the Mann-Whitney U test was utilized to evaluate the differences in medians. ResultsMale and female had significant differences regarding talking time when academic merit was taken into account.The mean talking time for female vs. male was 29.9 and 30.8 (SEM: 1.2 ± 1.7, 95% CI: -2.2 to 4.7).Mean years of practice for female vs. male was 16.7 and 25.3 (SEM: 8.6 ± 1.1, 95% CI: 6.4 to 10.9).Mean number of publications for female vs. male was 40.5 and 84.0 (p< 0.01).Median number of citations for female vs. male was 707.5 and 1582 (p<0.01).Median h-index for female vs. male was 12.0 and 19.50 (p<0.01). ConclusionsOur study showed that there is a gender bias present in North American ob/gyn conferences, where being a male is associated with less talking time than females of equal academic merit.Further studies are needed to elucidate the true effects of gender on opportunities at ob/ gyn meetings.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.438 | 0.213 |
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".