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Racial Stress, Racial Trauma, and Evidence-Based Strategies for Coping and Empowerment

2024· article· en· W4391757002 on OpenAlexaff
Samantha C. Holmes, Manzar Zare, Angela M. Haeny, Monnica T. Williams

Bibliographic record

VenueAnnual Review of Clinical Psychology · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRacismPsychologyCoping (psychology)EmpowermentMindfulnessCognitive restructuringEthnic groupCognitionSocial psychologyClinical psychologyStress managementPsychotherapistSociologyPsychiatryGender studies

Abstract

fetched live from OpenAlex

Racial stress and racial trauma refer to psychological, physiological, and behavioral responses to race-based threats and discriminatory experiences. This article reviews the evidence base regarding techniques for coping with racial stress and trauma. These techniques include self-care, self-compassion, social support, mindfulness, cognitive restructuring, cognitive defusion, identity-affirming practices and development of racial/ethnic identity, expressive writing, social action and activism, and psychedelics. These strategies have shown the potential to mitigate psychological symptoms and foster a sense of empowerment among individuals affected by racial stress and trauma. While the ultimate goal should undoubtedly be to address the root cause of racism, it is imperative to acknowledge that until then, implementing these strategies can effectively provide much-needed support for individuals affected by racism.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.006
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.261
GPT teacher head0.540
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2024
Admission routes1
Has abstractyes

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