Promoting Awareness to Counter Damaging Attitudes, Beliefs, and Reactions Related to Sexual Assault Against Trans People: A Social Media Campaign for Health and Social Service Providers
Bibliographic record
Abstract
Transgender (trans) people face high rates of sexual assault and often encounter systemic barriers in accessing appropriate care and supports, including, among others, stigma, discrimination, and a lack of provider knowledge. Trans communities and allies in research and the service sector have emphasized the potential of advocacy as a tool to dismantle barriers for trans people; however, to date, few advocacy efforts have been undertaken in the sexual assault context. To address this gap, we developed and implemented #TRANSformativeKnowledge, a social media campaign to promote awareness among providers about the damaging attitudes, beliefs, and reactions that often impede trans survivors' access to appropriate services. Based on insights from a recorded consultation with trans community members and health and social service professionals, we designed seven posters for circulation on Twitter, each containing a representative quote, key message, and associated call to action. The campaign was launched May 17, 2021, with posters Tweeted twice weekly, including one final summary post on June 30, 2021. The campaign reached approximately 100,000 Twitter users, with almost 2,000 engagements. As demonstrated by these findings, our social media advocacy campaign represents a viable method for disseminating knowledge about sexual assault against trans people, which could be replicated by others aspiring to advance health equity through advocacy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".