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Record W1548623541 · doi:10.1080/00981389.2011.588115

Substance Use Among Asian-American Adolescents: Perceptions of Use and Preferences for Prevention Programming

2011· article· en· W1548623541 on OpenAlexaff
Lin Fang, Kevin Barnes-Ceeney, Rebecca A. Lee, John Tao

Bibliographic record

VenueSocial Work in Health Care · 2011
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
FundersNational Institute on Drug AbuseNational Taiwan University
KeywordsSubstance usePerceptionSubstance abuse preventionPsychologyAsian americansSubstance abuseClinical psychologyMedicineFamily medicineSocial psychologyPsychiatryPolitical scienceEthnic group

Abstract

fetched live from OpenAlex

Rarely has substance use prevention programming targeted Asian-American adolescents. Using a focus group methodology, we explored perceptions of substance use and preferences for prevention programming among 31 Asian-American adolescents in New York City. Participants considered substance use common in the community. Factors contributing to substance use among Asian-American adolescents (e.g., peer pressure, pressure to achieve, family factors, and community influence) were identified, and the need for prevention programs tailored for the Asian-American community was highlighted. Participants discussed preferred program content, delivery settings, and recruitment and retention strategies. Despite the favorable attitude for family-based prevention programming, participants raised potential issues concerning the feasibility of such a program. Study findings facilitate understanding of Asian-American adolescents' substance use behavior and shed light on prevention program development for this underserved population.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.075
GPT teacher head0.347
Teacher spread0.273 · 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 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

Citations12
Published2011
Admission routes1
Has abstractyes

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