Expanding Opportunities for Professional Development: Utilization of Twitter by Early Career Women in Academic Medicine and Science
Bibliographic record
Abstract
The number of women entering medical school and careers in science is increasing; however, women remain the minority of those in senior faculty and leadership positions. Barriers contributing to the shortage of women in academics and academic leadership are numerous, including a shortage of role models and mentors. Thus, achieving equity in a timelier manner will require more than encouraging women to pursue these fields of study or waiting long enough for those in the pipelines to be promoted. Social media provides new ways to connect and augments traditional forms of communication. These alternative avenues may allow women in academic medicine to obtain the support they are otherwise lacking. In this perspective, we reflect on the role of Twitter as a supplemental method for navigating the networks of academic medicine. The discussion includes the use of Twitter to obtain (1) access to role models, (2) peer-to-peer interactions, and continuous education, and (3) connections with those entering the pipeline-students, trainees, and mentees. This perspective also offers suggestions for developing a Twitter network. By participating in the "Twittersphere," women in academic medicine may enhance personal and academic relationships that will assist in closing the gender divide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".