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Record W4309623590 · doi:10.1080/87568225.2022.2145252

Psychological Distress in Treatment-Seeking University Students: An Intersectional Examination of Asian Identity and Gender Identity

2022· article· en· W4309623590 on OpenAlexaff
Maryam Sorkhou, Tayyab Rashid, Jessica Dere, Amanda A. Uliaszek

Bibliographic record

VenueJournal of College Student Mental Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsThe Scarborough HospitalUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsEthnic groupPsychological distressPsychologyClinical psychologyPsychological interventionDistressIdentity (music)Mental healthPsychiatrySociology

Abstract

fetched live from OpenAlex

We sought to elucidate the presentation of psychological distress across treatment-seeking university students at the intersection of gender and ethnic identity, concentrating on East and South Asian identity. Using retrospective baseline data from 1530 university students utilizing on-campus counseling services, ANOVAs were conducted to evaluate the effects of gender and ethnic identity on total and subscale scores of The Outpatient Questionnaire-45 (OQ-45). Compared to White students, South and East Asians exhibited significantly elevated levels of psychological distress. Although no gender differences emerged across overall psychological distress, there was a significant interaction between gender and ethnicity on total OQ-45 and certain subscales of this measure. Our findings provide important insight towards the limited body of evidence analyzing the intersection of gender and ethnicity in mental health. Revealing how multiple identities contribute to the presentation of psychological distress in postsecondary students can lead to the implementation of more effective interventions.

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)0.004
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 score0.026
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.085
GPT teacher head0.469
Teacher spread0.384 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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