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Record W3174572909 · doi:10.3390/ijerph18136759

Family Support and Sociocultural Factors on Depression among Black and Latinx Sexual Minority Men

2021· article· en· W3174572909 on OpenAlexaff
Donté T. Boyd, S. Raquel Ramos, Camille R. Quinn, Kristian Jones, Leo Wilton, LaRon E. Nelson

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsSt. Michael's Hospital
FundersNational Institute on Drug AbuseNational Institute of Mental Health
KeywordsSociocultural evolutionDepression (economics)PsychologySexual minorityBlack femaleDevelopmental psychologyDemographyGender studiesSocial psychologySociologySexual orientationAnthropology

Abstract

fetched live from OpenAlex

Family-based approaches are critical for improving health outcomes in sexual minority men (SMM) of color. Yet, it is unclear how family context, internalized homophobia, and stress influence mental health outcomes among sexual minority men of color. From a cross-sectional sample of 448 participants, aged 16–24 years, survey data were analyzed to examine rates of family social support, the perception of sexuality by family, the stressfulness of life events, internalized homophobia, and other contextual variables on depression using linear regression. Our results indicated that an 86% increase in family social support was related to a −0.14 decrease in depression (ß = −0.14, p = 0.004). In addition, SMM who were separated by family and friends because of their sexuality were statistically significant and positively associated with depression (ß = 0.09, p < 0.001). Findings from our study suggest that the influence from the microsystem is salient in modifying mental health outcomes for SMM of color.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.441
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207