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Record W2135865414 · doi:10.3109/09687630903514909

Alcohol use in seven ethnic communities in Ontario: A qualitative investigation

2010· article· en· W2135865414 on OpenAlexaffabout
Branka Agic, Robert E. Mann, Marianne Kobus-Matthews

Bibliographic record

VenueDrugs Education Prevention and Policy · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsEthnic groupFocus groupCultural diversityVulnerability (computing)SomaliSocioeconomic statusPsychologyEnvironmental healthMedicineSociologyPopulation

Abstract

fetched live from OpenAlex

Populations in Canada represent a diverse range of cultures with different beliefs and norms regarding alcohol use and related problems. While there is very little published research on the cultural aspects of alcohol and other substance uses in Canada, in spite of the cultural diversity of the country, there are important indications that alcohol is a serious problem in many ethnic communities. In order to arrive at a more complete understanding of the issues related to providing culturally sensitive approaches that would meet the alcohol-related health promotion needs of diverse communities, focus group discussions were conducted with the key informants and community members from seven Ontario communities: the Polish, Portuguese, Russian, Tamil, Punjabi, Serbian and Somali. The results revealed that the types and sizes of alcoholic beverages consumed in each community, drinking levels that are considered ‘normal’ or ‘excessive’, as well as the perception of alcohol-related problems are largely shaped by their cultural norms and beliefs, which often differ from those of the dominant culture. Health messages that reflect the dominant culture are often not relevant to people from different cultural backgrounds. Socioeconomic disadvantages and barriers to service utilization heighten their vulnerability to alcohol problems. These findings have important implications for prevention and service provision, particularly to ethnic communities that may be unlikely to access services through more standard channels.

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 score0.569
Threshold uncertainty score0.886

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 head0.102
GPT teacher head0.422
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; 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

Citations20
Published2010
Admission routes2
Has abstractyes

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