How Experiences of Stigma and Discrimination at Various Points in the Queer Life Course Interrelate with Alcohol Use: Views from Sexually and Gender Diverse Youth
Bibliographic record
Abstract
BACKGROUND: The life courses of sexually and gender diverse individuals are shaped by a series of events that include acceptance of one's own sexual orientation or gender identity, the coming out process and socialization in the LGBTQ+ (for Lesbian, Gay, Bisexual, Transgender, Queer/Questioning, and other sexual orientations and gender identities represented by the plus sign) environment. Generally experienced in a cis-heteronormative context, this process is marked by stigma and discrimination and the social harms they can cause, including a higher-than-average prevalence of alcohol use. OBJECTIVES: To profile the alcohol use of sexually and gender diverse (SGD) youth from a life course perspective and to explore individual perceptions regarding the personal, social and cultural factors (including stigmatization and its consequences) modulating their consumption. METHODOLOGY: This qualitative descriptive study is grounded in symbolic interactionism. Semi-structured interviews lasting approximately 90 min were conducted with LGBTQ+ youth aged 18 to 30 using alcohol at least once a week. A thematic analysis was performed. RESULTS: A total of 31 individuals aged 18-29 (average age: 25) were interviewed. The average score regarding alcohol use was 14.25 (Standard Deviation -SD-: 4-31), which corresponds to a moderate risk and indicates the need for a brief intervention. Our study documents how the higher alcohol use among LGBTQ+ youth is shaped by individual, community and cultural factors at different points in the queer life course. Among the factors influencing drinking are the emotions experienced when questioning sexual orientation and/or gender identity as well as the feelings resulting from stigma and discrimination. Our findings also indicate the influence of socializing in the queer community and meeting peers and partners, as well as that of LGBTQ+ cultural practices. DISCUSSION: Our study indicates the need for grassroots-level interventions that work to mitigate social pressures in queer socialization contexts. Accordingly, any intervention, whether preventive or therapeutic, must consider the interplay of personal, social, community and cultural factors. Interventions regarding alcohol use must build on the strengths of community and the sense of belonging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".