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Record W4284891635 · doi:10.15273/jue.v12i2.11412

“Black Students Do the Real Work!”: Maintaining Mental Health Among Black College Students at UCLA

2022· article· en· W4284891635 on OpenAlexvenueno aff
Princess Udeh

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2022
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthRacismStressorPsychologyBlack maleMedical educationResource (disambiguation)PedagogyMedicineGender studiesSociologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Black college students deal with academic and racial stressors due to the racism they experience at Predominantly White Institutions (PWI). Mental health care resources are universally available at UCLA; however, Counseling and Psychological Services (CAPS), the primary resource, is a mental health hub for 33,000+ students at UCLA. In this study, I explore how Black college students at UCLA view CAPS and utilize Black-run campus organizations to create their own “safe space.” Through a mixedmethods approach, I found that Black students do not utilize counseling resources because they are unwelcoming and there is a lack of culturally trained psychologists or Black psychologists available to discuss the imposter syndrome, microaggressions, and racism Black students experience. As a result, Black students take on the role of community organizers. Through the creation and maintenance of the Afrikan Student Union and other Blackrun campus organizations, Black students create safe spaces for themselves and provide race-based resources to maintain retention within their community.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0120.004
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.003
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 head0.036
GPT teacher head0.375
Teacher spread0.339 · 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 designQualitative
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

Citations2
Published2022
Admission routes1
Has abstractyes

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