Studies to Improve Perinatal Health through Diet and Lifestyle among South Asian Women Living in Canada: A Brief History and Future Research Directions
Bibliographic record
Abstract
South Asians (i.e., people who originate from India, Pakistan, Sri Lanka, Nepal, and Bangladesh) have higher cardiovascular disease rates than other populations, and these differences persist in their offspring. Nutrition is a critical lifestyle-related factor that influences fetal development, and infant and child health in early life. In high-income countries such as Canada, nutrition-related health risks arise primarily from overnutrition, most strikingly for obesity and associated non-communicable diseases. Evidence for developmental programming during fetal life underscores the critical influence of maternal diet on fetal growth and development, backed by several birth cohort studies including the Pune Maternal Nutrition Study, the South Asian Birth Cohort Study, and the Born in Bradford Study. Gestational diabetes mellitus is a strong risk factor for type 2 diabetes, future atherosclerosis and cardiovascular disease in the mother and increases the risk of type 2 diabetes in her offspring. Non-pharmacological trials to prevent gestational diabetes are few, often not randomized, and are heterogeneous with respect to design, and outcomes have not converged upon a single optimal prevention strategy. The aim of this review is to provide an understanding of the current knowledge around perinatal nutrition and gestational diabetes among the high-risk South Asian population as well as summarize our research activities investigating the role of culturally-tailored nutrition advice to South Asian women living in high-income settings such as Canada. In this paper, we describe these qualitative and quantitative studies, both completed and underway. We conclude with a description of the design of a randomized trial of a culturally tailored personalized nutrition intervention to reduce gestational glycaemia in South Asian women living in Canada and its implications.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".