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Record W2944054509 · doi:10.1177/0022146519845069

The Black-White Paradox Revisited: Understanding the Role of Counterbalancing Mechanisms during Adolescence

2019· article· en· W2944054509 on OpenAlexaff
Patricia Louie, Blair Wheaton

Bibliographic record

VenueJournal of Health and Social Behavior · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDistressPsychologyMental healthMoodAssociation (psychology)Self-esteemClinical psychologyRace (biology)White (mutation)Mood disordersDevelopmental psychologyAnxietyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

The tendency for blacks to report similar or better mental health than whites has served as an enduring paradox in the mental health literature for the past three decades. However, a debate persists about the mechanisms that underlie this paradox. Drawing on the stress process framework, we consider the counterbalancing roles of self-esteem and traumatic stress exposure in understanding the "black-white paradox" among U.S. adolescents. Using nationally representative data, we observe that blacks have higher levels of self-esteem than whites but also encounter higher levels of traumatic stress exposure. Adjusting for self-esteem reveals a net higher rate of mood disorders and distress among blacks relative to whites, and differences in traumatic stress exposure mediate this association. In the full model, we show that self-esteem and stress exposure offset each other, resulting in a null association between race and mood disorders and a reduced association between race and distress.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.004
Open science0.0010.002
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 head0.037
GPT teacher head0.354
Teacher spread0.317 · 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

Citations52
Published2019
Admission routes1
Has abstractyes

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