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Record W2604694561 · doi:10.1177/0265407517699713

The buffering effect of peer support on the links between family rejection and psychosocial adjustment in LGB emerging adults

2017· article· en· W2604694561 on OpenAlexfundno aff
Luis A. Parra, Timothy S. Bell, Michael Benibgui, Jonathan L. Helm, Paul D. Hastings

Bibliographic record

VenueJournal of Social and Personal Relationships · 2017
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
FundersNational Institute of Mental HealthNational Institutes of HealthConcordia University
KeywordsPsychologyPsychosocialSocial supportPeer supportAnxietyContext (archaeology)Clinical psychologyLesbianFamily supportSocial anxietyPeer groupDepression (economics)HomosexualityMental healthDevelopmental psychologyPsychiatrySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Lesbian, gay, and bisexual (LGB) emerging adults often seek support from their peers if they lack support from their family of origin. We predicted that peer social support would moderate the link between negative family relationships and psychosocial adjustment, such that in the context of family rejection, experiencing more peer support would predict lower levels of anxiety, depression, and internalized homonegativity (IH) and higher self-esteem. Sixty-two (27 females) LGB individuals (ages 17–27, M = 21.34 years, SD = 2.65) reported on their families’ attitudes toward homosexuality, experiences of family victimization, peer social support, anxiety and depression symptoms, IH, and self-esteem. Results showed that peer social support moderated the link between negative family attitudes and anxiety and also moderated the link between family victimization and depression. The moderating effects suggest that having a supportive peer group may protect against mental health problems for LGB emerging adults who lack support from their family of origin.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.080
GPT teacher head0.393
Teacher spread0.313 · 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

Citations87
Published2017
Admission routes1
Has abstractyes

Explore more

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