Addressing discrimination and violence against Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ) persons from Brazil: a mobile health intervention
Bibliographic record
Abstract
BACKGROUND: Sexual and gender minorities (SGM) experience higher rates of discrimination and violence when compared to cis, heterosexual peers. However, violent crimes and other hate incidents against SGM persons are consistently not reported and prosecuted because of chronic distrust between the SGM community and police. Brazil is one of the most dangerous countries for SGM persons globally. Herein, we describe the development of a mobile health intervention to address the rampant violence against this population, the Rainbow Resistance-Dandarah app. METHODS: We conducted community-based participatory research (CBPR) between 2019 and 2020. The study started with in-depth interviews (IDIs) and focus group discussions (FGDs) with representatives of the SGM community from Brazil. Descriptive qualitative data analysis included the plotting of a 'word cloud', to visually represent word frequency, data coding and analysis of more frequent themes related to app acceptability, usability, and feasibility. A sub-sample of SGM tested the app and suggested improvements, and the final version was launched in December 2019. RESULTS: Since the app was launched in December 2019, the app recorded 4,114 active SGM users. Most participants are cisgender men (50.9%), self-identified as gay (43.5%), White (47.3%), and aged 29 or less (60.9%). FGDs and IDIs participants discussed the importance of the app in the context of widespread violence toward SGM persons. Study participants perceived this mHealth strategy as an important, effective, and accessible for SGM surviving violence. The CBPR design was highlighted as a key strategy that allowed SGM persons to collaborate in the design of this intervention actively. Some users reported how the panic button saved their lives during violent attacks. CONCLUSIONS: Rainbow Resistance-Dandarah app was endorsed as a powerful tool for enhancing reporting episodes of violence/discrimination against SGM persons and a key strategy to connect users with a safe network of supportive services. Results indicate that the app is an engaging, acceptable, and potentially effective mHealth intervention. Participants reported many advantages of using it, such as being able to report harassment and violence, connect with a safe network and receive immediate support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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 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".