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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».