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Record W4411655425 · doi:10.3390/bs15070856

Treating Racial Trauma: The Methodology of a Randomized Controlled Trial of the Healing Racial Trauma Protocol

2025· article· en· W4411655425 on OpenAlexafffund
Muna Osman, Sophia Gran-Ruaz, Monnica T. Williams

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsRandomized controlled trialRacismMedicineProtocol (science)Psychological traumaClinical psychologyPopulationMental healthPsychiatryPsychologyAlternative medicineSurgery

Abstract

fetched live from OpenAlex

Cumulative experiences of racism lead to stress and trauma. Racial trauma is associated with compromised functioning across psychological, social, and physical health domains. This is further complicated by any comorbidity with other mental health conditions. Many clinicians are not trained in identifying, diagnosing, and treating racial trauma. Given the pervasive nature of racism, limited clinician knowledge and experiences, as well as the impact of this condition, there is an urgent need for novel, culturally safe, and effective treatment. The newly developed Healing Racial Trauma Protocol (HRTP) shows significant promise. We explore the methodological considerations for a randomized controlled trial comparing the efficacy of the HRTP for racialized individuals suffering from racial trauma and to a control condition of treatment as usual, in reducing the severity of racial trauma and depression symptoms, as well as improved functioning. Ethical, pragmatic, and methodological considerations in trial design, research population, and treatment protocol are explored.

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)0.027
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.006
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.211
GPT teacher head0.532
Teacher spread0.320 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
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

Citations3
Published2025
Admission routes2
Has abstractyes

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