Alcohol use in seven ethnic communities in Ontario: A qualitative investigation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".