Dealing with gender-related and general stress: Substance use among Brazilian transgender youth
Bibliographic record
Abstract
INTRODUCTION: Adolescent substance use is a major public health concern since it enhances adolescent morbidity and mortality, affecting adulthood health and well-being. Although current evidence shows a high risk for substance use among transgender populations, to date, few studies evaluate substance use among transgender youth. METHOD: Brazilian transgender youth (ages between 16 and 25 years old) answered an online questionnaire measuring demographics, substance use and modifiable factors associated with drug use to deal with general stress, gender-related stress, and recreational use. RESULTS: Cannabis was the most frequent substance used among transgender youth (20.88%; CI 95% 23.71-36.19), whereas 11.45% (CI 95% 11.38-21.47) of volunteers disclosed use of pain medication, such as codeine, and 5.05% (CI 95% 3.71-10.78) revealed use of sedatives and tranquilizers in the last 30 days. ADH medication (not prescribed), as well as cocaine and other drugs (such as antihistamines and Hookah), was also reported by 2.36% (CI 95% 0.92-5.84), 2.69% (CI 95% 1.24-6.49) and 4.04% (CI 95% 2.61-8.98) of transgender youth. CONCLUSION: A logistic regression model showed that discrimination and home instability were the primary determinants of vulnerable to substance use among youth. Therefore, the harm reduction strategies must affect the social and physical aspects of transgender youth lives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".