Social Support–Seeking Strategies on Social Media at the Intersection of Lesbian, Gay, Bisexual, Transgender, and Queer Identity, Race, and Ethnicity: Insights for Intervention From a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, transgender, and queer (LGBTQ+) individuals experience a disproportionately higher prevalence of mental health challenges when compared to their heterosexual and cisgender counterparts. Moreover, they exhibit increased engagement with social media platforms relative to their peers. Understanding the intersectional dynamics of their identities is crucial in elucidating effective and safe approaches to garnering social support through social media channels. This exploration holds significance for informing future research endeavors and shaping targeted interventions to address the unique mental health needs of LGBTQ+ individuals. OBJECTIVE: The purpose of this study was to explore the strategies used by Black, Hispanic, and non-Hispanic White LGBTQ+ young adults to acquire social support from social media. The study aimed to examine how these strategies may differ by race and ethnicity. METHODS: We conducted semistructured interviews with LGBTQ+ young adults aged between 18 and 30 years recruited in the United States from social media. Of 52 participants, 12 (23%) were Black, 12 (23%) were Hispanic, and 28 (54%) were non-Hispanic White. Thematic analysis was used to analyze the collected data. RESULTS: The analysis uncovered both divergent and convergent strategies among participants of different races and ethnicities. Black and Hispanic young adults exhibited a preference for connecting with individuals who shared similar identities, seeking safety and tailored advice. Conversely, non-Hispanic White participants demonstrated minimal preference for identity-based advice. Seeking support from anonymous sources emerged as a strategy to avoid unwanted disclosure among Hispanic participants. Furthermore, all participants emphasized the importance of content filtering with family members to cultivate positive and supportive social media experiences. CONCLUSIONS: This study sheds light on the strategies used by LGBTQ+ individuals of different racial and ethnic backgrounds to seek social support from social media platforms. The findings underscore the importance of considering race and ethnicity when examining social support-seeking behaviors on social media in LGBTQ+ populations. The identified strategies provide valuable insights for the development of interventions that aim to leverage social support from social media to benefit the mental health of Black, Hispanic, and non-Hispanic White LGBTQ+ young adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".