LGBTQ Stigma
Bibliographic record
Abstract
Abstract Lesbian, gay, bisexual, transgender, and/or queer (LGBTQ) individuals face significant stigma globally. Examples of stigma range from extreme acts of violence, such as murder, to more subtle yet pervasive forms of marginalization and social exclusion, such as being socially rejected, denied employment opportunities, and given poor healthcare. Stigma has been identified as a fundamental cause of global LGBTQ health inequities. This chapter summarizes research on and theory that defines LGBTQ stigma, documents ways in which stigma is manifested and experienced by LGBTQ individuals, articulates how stigma leads to health inequities among LGBTQ populations, and identifies evidence-based intervention strategies to address LGBTQ stigma. Moreover, recommendations for addressing stigma to promote LGBTQ health equity globally are provided. As examples, promoting policy change and investing in social norm campaigns can reduce stigma at the structural level, enhancing education and providing opportunities for interpersonal contact can reduce stigma among individuals who perpetrate stigma, and bolstering resilience can protect LGBTQ individuals from stigma. Intervention strategies that have been developed in the Global South are being applied in the Global North (e.g., participatory theatre) and vice versa. As the field moves toward addressing stigma to achieve LGBTQ health equity, it is worth bearing in mind that stigma is neither fixed nor insurmountable. Rather, it is malleable and intervenable: it has changed and will continue to change with time. Public health researchers, practitioners, policy makers, and other stakeholders have key roles to play in advocating for continued change in LGBTQ stigma worldwide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
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".