How to Be Savvy About Gender Disparities in Academic Stroke Medicine
Bibliographic record
Abstract
In the past decade, stroke medicine has evolved from discovery of innovative diagnostic tools to implementation of new treatments. These advances are projected to increase the demand for stroke neurologists in academic and clinical practices, but hopefully with equitable opportunities for everyone across the gender spectrum. Academic medicine provides opportunities to participate in clinical care, teaching, research, and administration. The early career stage is short-focused on finding an academic niche and developing new skills that will help you navigate the academic environment. A recent InterSECT article emphasized the critical role of women's leadership in stroke medicine. In this article, we reflect on workforce gender disparities and provide 5 practical strategies that may help women overcome barriers and advance their work mission.
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 | 0.044 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".