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Record W2333130235

Considering the nuances of working with South Asian Punjabi families around substance abuse

2015· article· en· W2333130235 on OpenAlexaboutno aff
Jessica Sandhu

Bibliographic record

VenueThe Atrium (University of Guelph) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologySubstance abusePsychologySocial psychologyPolitical sciencePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

According to Statistics Canada (2013), in 2012, approximately 21.6% of the Canadian population met the criteria for a substance abuse disorder at some point during their lifetime. This speaks to the importance of having evidence-supported treatments (ESTs) available to inform the work of therapists when working with this clinical population and their families. However, there is a gap in the research literature on ESTs for substance abuse in that cultural factors have been underexamined and the South Asian community is consistently excluded from research. This is alarming as the South Asian population is one of the two largest visible minority populations in Canada (Nakamura, Lalomiteanu, Rehm & Fischer, 2011). The current paper examines the beliefs held by the South Asian community around substance abuse, reviews the literature on substance abuse, its effects on the family system and the treatment options available, explores the cultural factors that must be considered when conceptualizing substance abuse, considers the implications of the lack of ESTs for substance abuse for the South Asian Punjabi population, and discusses how therapists can be culturally competent in their work with South Asian Punjabis.

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 score0.018
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.129
GPT teacher head0.305
Teacher spread0.176 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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