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Record W3206376478 · doi:10.25071/1929-8471.78

The Substance Abuse Program for African-Canadian and Caribbean Youth (SAPACCY): An Innovative Program Serving the Mental Health Needs of African, Caribbean, and Black Youth

2021· article· en· W3206376478 on OpenAlexaffabout
Amy Gajaria, Kevin Haynes, Yolanda Kosic, Donna Maria Alexander

Bibliographic record

VenueINYI Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsMental healthSubstance abuseAddictionPsychiatryPsychologyMedicine

Abstract

fetched live from OpenAlex

Black youth experience disproportionately poor health outcomes throughout Ontario's healthcare system, including the mental health and addictions system. The Substance Abuse Program for African Canadian and Caribbean Youth (SAPACCY) at the Centre for Addiction and Mental Health (CAMH) seeks to address this disparity by providing clinical services to youth who identify as Black and/or as having African and/or Caribbean heritage, and their families, who are struggling with problematic substance use and/or mental health concerns. The clinical team works from an Afrocentric, culturally responsive lens to promote recovery and support Black youth in working through their mental health and addiction concerns. The program offers mental health and addictions counselling and psychotherapy, psychiatric consultation, psychoeducation, resource navigation, advocacy, and case management services to assist youth and their families/caregivers in reducing harm, moving toward recovery, and making healthy choices for themselves and their family. This paper will discuss SAPACCY’s approach to helping clients build resilience and resistance to anti-Black 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 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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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 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
Published2021
Admission routes2
Has abstractyes

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