Mental health inequalities among LGBT older people in the United States: curricula developments
Bibliographic record
Abstract
Introduction Large-scale studies and national population-based surveys have been used to explore the interactions between sexual orientation, gender identity and mental health outcomes in the United States (US), Canada, and other English-speaking countries. These studies illuminate the ways in which lesbian, gay, bisexual and trans (LGBT) adults experience inequalities in mental health outcomes when compared with their heterosexual peers. For example, lesbians and bisexual women are at greater lifetime risk for substance abuse and dependence than heterosexual men and women (King et al, 2008). Gay men experience higher rates of mood and anxiety disorders than their heterosexual male peers or lesbians (Bostwick et al, 2010). Bisexual women are more likely to report poorer outcomes related to mood, anxiety, and suicide than their heterosexual, lesbian or gay counterparts (Steele et al, 2009). Trans individuals cite discrimination, negative body image and the complexity of intimate partner relationships as significant factors affecting mental health (Bockting et al, 2006). One additional factor not included in these studies is the effects of ageing on mental health outcomes within the LGBT population. While Fredriksen-Goldsen and Muraco (2010) specify the need to focus on the additional factors of age, cohort affect, culture and individual life experiences when studying any aspect of the LGBT population, few studies have utilised these when exploring mental health inequalities among LGBT adults. This chapter describes how incorporating these factors will illuminate ways in which LGBT mental health inequalities may shift over the lifecourse and offers suggestions for developing best practices. Sexual orientation, gender identity and mental health outcomes Despite enjoying access to similar economic resources than their heterosexual counterparts, LGBT adults living in the US experience significant inequalities in mental health outcomes when compared with their heterosexual peers. Numerous studies explore the rates of depression, anxiety, suicidality, substance abuse and self-harm in the LGBT population (Cochrane and Mays, 2006; Herek and Garnets, 2007; Institutes of Medicine, 2011). In a meta-analysis of lesbian, gay and bisexual (LGB) mental health studies published worldwide, King et al (2008) compared these rates to existing data on non-LGB populations. They determined that the risk for depression and anxiety were 1.5 times more likely in LGB individuals, that lesbians and bisexual women experienced a higher risk of substance dependence and the lifetime prevalence of suicide rates was substantially higher in gay and bisexual men. Studies of mental health and trans adults are few.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".