Paediatric health care in the #MeToo era: Advocating for survivors of sexual violence
Bibliographic record
Abstract
We are witnessing a momentous cultural shift in how we understand, respond to, and resist sexual violence. Rise of the #MeToo movement has ignited a viral wave of consciousness raising, dialogue, and advocacy on an international scale. Women and girls have been empowered to share their stories of sexual assault, bringing to light the widespread prevalence of gender-based violence. As the shame and blame that have silenced victims gradually diminish, we anticipate a continued upward trend in sexual assault disclosure among youth, and corresponding increase in demand for trauma-informed paediatric sexual assault services. It is our collective responsibility to prevent revictimization and retraumatization by the very systems designed to help. In this critical lens commentary, we strongly advocate for heightened awareness and improved responsiveness among paediatric health care providers and policymakers to effectively and ethically meet the diverse needs of the growing number of young survivors who take the brave step of speaking out and reaching out.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".