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Record W4401049858 · doi:10.1177/21568693241266960

Intersectional Social Support: Gender, Race, and LGBTQ Youth Friendships

2024· article· en· W4401049858 on OpenAlexaff
Brandon Andrew Robinson, Fei Mu, Javania Michelle Webb, Amy L. Stone

Bibliographic record

VenueSociety and Mental Health · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsMcMaster University
FundersNational Science Foundation
KeywordsTransgenderMental healthPsychologySocial supportMinority stressLesbianSexual minoritySexual orientationQueerHuman sexualityIntersectionalityQualitative researchGender studiesHeterosexismSocial psychologyDevelopmental psychologySociologyPsychotherapist

Abstract

fetched live from OpenAlex

Lesbian, gay, bisexual, transgender, and queer (LGBTQ) youth experience disproportionate mental health challenges due to minority stress. Little research, however, has considered how social support from intragenerational friends impacts the mental health of LGBTQ youth, particularly for LGBTQ youth of color. Based mainly on qualitative interviews from a longitudinal study with 83 LGBTQ youth from California and Texas, we develop the concept of intersectional social support —how multiply marginalized individuals subjectively interpret social support and how they view social support from similar multiply marginalized others. More specifically, the findings of this study capture how the intersecting identities of age, sexuality, gender, and race can shape the meanings and experiences of receiving familial support, emotional support, informational support, and instrumental support. This study is an important contribution to understanding how intersecting identities influence how people perceive social support practices and manage their mental health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.394
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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