Qualitative Exploration of the #MeTooMedicine Online Discourse: “Holding Beacons of Light to Shine in the Corners They Are Hoping to Keep Dark”
Bibliographic record
Abstract
PURPOSE: The MeToo movement forced a social reckoning, spurring women in medicine to engage in the #MeTooMedicine online discourse. Given the risks of reporting sexual violence, discrimination, or harassment, it is important to understand how women in medicine use platforms like Twitter to publicly discuss their experiences. With such knowledge, the profession can use the public documentation of women in medicine for transformative change. METHOD: Using reflexive thematic analysis, 7,983 tweets (posted between November 2017 and January 2020) associated with #WomenInMedicine, #MeTooMedicine, and #TimesUpHC were systematically analyzed in 2020-2022, iteratively moving from describing their content, to identifying thematic patterns, to conceptualizing the purpose the tweets appeared to serve. RESULTS: The Twitter engagement of women in medicine was likened to "holding beacons of light to shine in the corners [harassers] are hoping to keep dark," both reinforcing the message that "gender bias is alive and well" and calling for a "complete transformation in how we approach" the problem. The tweets of women in medicine primarily seemed aimed at disrupting complacency; encouraging bystanders to become allies; challenging stereotypes about women in medicine; championing individual women leaders, peers, and trainees; and advocating for reporting mechanisms and policies to ensure safety and accountability across medical workplaces. CONCLUSIONS: Women in medicine appeared to use Twitter for a host of reasons: for amplification, peer support, advocacy, and seeking accountability. By sharing their experiences publicly, women in medicine seemed to make a persuasive argument that time is up, providing would-be allies with supporting evidence of sexual violence, discrimination, and harassment. Their tweets suggest a roadmap for what is needed to achieve gender equity, ensure that lack of awareness is no longer an excuse, and ask bystanders to grapple with why women's accounts continue to be overlooked, ignored, or dismissed and how they will support women moving forward.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".