+Fièr : une application mobile pour aider les jeunes issus de la communauté LGBTQ+ et leur famille
Bibliographic record
Abstract
LGBTQ+ people are anywhere from 1.5 to 4 times more likely than heterosexual people to report depression, anxiety, suicidal behaviors, substance abuse, eating disorders, risky sexual behaviors, homelessness, and victimization. Objective The purpose is to describe the development of a mobile application for LGBTQ youth and their family. This article is part of a research program intended to equip LGBTQ+ youth and their families with technological tools to help them foster adaptive strategies in the face of stigma. LGBTQ+ youth face unique stressors both publicly (e.g. victimization) as well as personally (e.g. identity development and "coming out" process). Method We build upon Isabelle Ouellet-Morin's team +Fort: Stronger than Bullying © mobile application designed to reduce victimization among youth. We will create a new app called +Fièr/+Proud, to be designed and piloted in collaboration with LGBTQ+ participants ages 13-25 and their families. Impact Our hope is to bring LGBTQ+ youth together nationally and internationally to explore health promoting coping strategies, learn from custom training modules, share their unique experiences, and help inform parents of the experiences that LGBTQ+ people often face and fight in silence.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".