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Record W4407562733 · doi:10.1521/jscp.2025.44.1.001

Bipolar spectrum risk and social network dimensions in emerging adults: Two social sides?

2025· article· en· W4407562733 on OpenAlexaff
Stevi G. Ibonie, Gerald Young, Montana L. Ploe, Iris B. Mauss, Lauren B. Alloy, Jessica L. Borelli, Ben Bullock, Sarah R. Holley, Ellen Jopling, Shanmukh V. Kamble, Joelle LeMoult, Liam Mason, Daniel P. Moriarity, Robin Nusslock, Amie Okuma, Robb B. Rutledge, Gregory P. Strauss, Cynthia Villanueva, June Gruber

Bibliographic record

VenueJournal of Social and Clinical Psychology · 2025
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of British Columbia
FundersMedical Research Council
KeywordsPsychologySocial riskSpectrum (functional analysis)Developmental psychologyEnvironmental healthMedicinePhysics

Abstract

fetched live from OpenAlex

Introduction: Bipolar spectrum disorders (BSDs) encompass severe and chronic mood disorders associated with social functioning difficulties. However, little work has examined more nuanced aspects of social functioning in BSDs. Methods: This investigation recruited 1,934 emerging adult college students to examine associations of self-reported bipolar spectrum risk (including both BSD risk and current mania and depressive mood symptoms) with social functioning with peers (including social network quantity and quality, social support, and social strain). Results: Self-reported BSD risk was associated with greater social strain, but also greater social network quantity (or size) and social support. Post-hoc results suggest that self-reported mood symptoms were similarly associated with increased social conflict, but also greater social network quantity (or size) and social support. Discussion: Taken together, these findings indicate a complex picture in which BSD risk and mood symptoms are associated with both social struggles as well as strengths. Implications for the involvement of social functioning in mood disturbance are discussed.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.432
Teacher spread0.395 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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