<i>Dietary habits and health beliefs</i>Of Chinese Canadians
Bibliographic record
Abstract
PURPOSE: The relationships among dietary behaviours, traditional health beliefs (THB), and demographic characteristics of Chinese Canadians living in Toronto were examined, as were their primary sources of nutrition information. METHODS: Through the use of probability sampling, 106 adult subjects who originated from China, Hong Kong, or Taiwan were recruited from five Chinese community organizations. A telephone interview, employing a tested questionnaire, was conducted in Cantonese or Mandarin. All data were analyzed with MS Excel and SPSS statistical software. RESULTS: Dietary acculturation is gradual and individual. Participants reported regular intakes of fruits and vegetables and fat-reducing behaviours. Most used both Chinese and Western cooking methods. Practices based on traditional Chinese health beliefs (THB), such as balancing yin and yang foods to promote health, were prevalent. Participants were grouped as THB-strong, THB-moderate, or THB-weak, on the basis of their health belief scores. Various significant relationships among the variables were identified. Chinese media, friends, and family were the primary sources of nutrition information; dietitians were identified by only 12%. CONCLUSIONS: This is the first study to apply a THB grouping for Chinese Canadians. Results will provide an important basis for nutrition interventions to encourage immigrants to make healthy food choices, using both traditional and Western foods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".