Exploring the Social Determinants of Health in Nutrition Care for South Asian Communities: A Narrative Review
Bibliographic record
Abstract
The South Asian (SA) diasporic communities in Canada experience a greater burden of diabetes and cardiovascular disease (CVD) compared to white populations. Nutrition interventions often focus on individual behaviours and fail to consider that the social determinants of health (SDH) have a greater impact on chronic disease risk. A narrative review was conducted to identify the SDH in nutrition care interventions for the SA diaspora in Canada. The final analysis included fourteen articles from which SDH were identified and categorized based on the Social Ecological Model (SEM). The study analysis yielded the following needs in dietetic practice based on the SEM: (1) intrapersonal - need for language appropriate services, and representation of cultural foods and non-Western health perspectives in dietary guidelines, (2) interpersonal - understanding family and friends as social supports, (3) community - incorporating peer and community leader influences, (4) institution - importance of faith-based locations as community hubs, and client workplaces as a barrier to attending appointments, and (5) policy - advocacy for transportation and childcare access, adequate and secure income, and equitable care. These findings urge dietitians to move beyond cultural awareness, sensitivity, and competence to practicing cultural safety and humility in their practice, which is integral to providing equitable care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".