The Dandarah App: An mHealth Platform to Tackle Violence and Discrimination of Sexual and Gender Minority Persons Living in Brazil
Bibliographic record
Abstract
Discrimination and violence are widely experienced by sexual and gender minority (SGM) persons worldwide. More than one SGM person is murdered every day in Brazil because of their sexuality or gender identity, which is the highest reported homicide rate in the world. Alt-hough discrimination and violence against SGM persons in Brazil are considered to be hate crimes, reporting is still suboptimal due to fear of police SGM phobia and victim blaming. Accessible and easily disseminated interventions are urgently needed. Herein, we describe the develop-ment of an mHealth solution to help address violence against SGM persons, namely the Rainbow Resistance: Dandarah App, with a synthesis of key results and feedback from the SGM community after 24 months of using the app. Twenty-two focus group discussions (FGDs) were conducted with SGM persons living in six Brazilian states: Bahia, Federal District, São Paulo, Rio de Janeiro, Minas Gerais, Sergipe, and Pará. A total of 300 SGM persons participated in the FGDs. A thematic analysis was performed to interpret the qualitative data. Content themes related to aesthetics, us-ability, barriers to resources, and likes/dislikes about the intervention arose from the FGDs. Participants found the intervention to be user-friendly, endorsed more likes than dislikes, and suggested a few changes to the app. The findings suggest that the intervention is usable and fit for future ef-fectiveness testing, and that it could fill an important gap in the well-being of SGM persons living in a country with high levels of discrimination and violence towards this community, i.e., Brazil.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".