Not So Micro: An Investigation of the Impact of Microaggressions on Sexual Risk Taking and Mental Health in South Asian and Other Racialized Gay, Bisexual, and Other Men Who Have Sex With Men
Bibliographic record
Abstract
Psychological distress and sexual risk taking (e.g., condomless anal sex; CAS) is disproportionately greater within the gay and bisexual (GBM) community, including racialized GBM (RGBM), in comparison to their heterosexual counterparts (Rodriguez-Seijas et al., 2019). These adverse health outcomes can be better understood as a result of repeated, harmful interactions between individuals belonging to stigmatized groups and discriminatory actions, informed by oppressive, systemic structures permeating individual experiences (Crenshaw, 1989; Meyer, 1995; Sue, 2007). Microaggressions, in particular, are a chronic, insidious form of stress commonly experienced by marginalized communities, particularly RGBM (Vaccaro & Koob, 2018). Although poor mental health outcomes have been associated with microaggressions, research investigating its impact on intersectional identities remains limited (Sadika et al., 2020). Furthermore, to date, no research has explicitly investigated sexual health outcomes (e.g., CAS, sexual consent) as it relates to microaggression experiences. The current PhD dissertation incorporated two studies, one quantitative (Study 1) and one qualitative (Study 2). Study 1 recruited 314 RGBM who completed a series of self report measures, including distal stressors (heterosexism in racialized communities, racism in same-sex romantic relationships, and racism in the LGBTQ community) experienced, degree of social support, proximal stressors (acceptance concerns, concealment motivation, internalized homonegativity) experienced, symptoms of depression and anxiety, and occurrence of CAS. Study 2 recruited 20 South Asian gay, bisexual, and other men who have sex with men (SAGBM) and investigated their lived experiences of microaggressions as it relates to health outcomes (sexual and mental) as well as coping strategies used to cope with microaggressions. Findings of both studies were interpreted through the theoretical lenses of both Intersectionality and the Minority Stress Model (Crenshaw, 1989; Meyer, 2003) and demonstrated a clear association between microaggressions and mental health outcomes (e.g., depression, anxiety, shame, anger), specifically highlighting proximal stressors as a mechanism through which psychopathology develops. Although in Study 1 there was no relationship found between CAS and microaggressions as well as a lack of support for social support as an effective moderated mediator, results from Study 2 contextualized these findings by highlighting that RGBM may use multiple coping strategies to address microaggressions. Furthermore, findings revealed the important power dynamics (e.g., racial, sexual positioning, immigrant status), which contribute to sexual risk taking and highlights examining sexual risk taking through an intersectional approach versus a single item measure (i.e., CAS). Clinical recommendations as well as outlining culturally consistent and strengths-based interventions addressing RGBM concerns (e.g., discussing body image and sexual racism) in an effort to reduce the impact of microaggressions will be discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".