<i>Dietary Education Tools for</i> South Asians with Diabetes
Bibliographic record
Abstract
PURPOSE: South Asian immigrants to Canada are at high risk for developing diabetes, and culturally relevant diet counselling tools are needed. We examined perceived needs and preferences for diet counselling resources based on the newly revised Canadian Diabetes Association meal planning guide. METHODS: Five focus groups of individuals from different regions of South Asia (n=53) discussed portion size estimating methods, cultural values and holidays, food group classifications, and common South Asian foods. A focus panel with dietitians (n=8) provided insight on current diabetes education methods and resources for teaching South Asian clients. RESULTS: The dietitian panel members reported a need for resources targeted at differing client skill levels. They also noted preferences for individual counselling, and common barriers to education including finances, access, South Asian diets, and cultural views on health. Community focus groups reported larger portions but fewer daily meals in Canada. Ingredients and portions were not measured. Fasting was an important value, and sweets were a crucial component of holidays. Resources in South Asian languages, inclusion of pictures, and separate legumes, sweets, and snacks food groups were preferred. CONCLUSIONS: Findings can be used when developing new counselling tools for the South Asian community.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".