Seafood consumption patterns, their nutritional benefits and associated sociodemographic and lifestyle factors among First Nations in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To describe seafood consumption patterns in First Nations (FN) in British Columbia (BC) and examine lifestyle characteristics associated with seafood consumption; to identify the top ten most consumed seafood species and their contributions to EPA and DHA intake; and to estimate dietary exposure to methylmercury, polychlorinated biphenyls and dichlorodiphenyldichloroethylene. DESIGN: Dietary and lifestyle data from the First Nations Food Nutrition and Environment Study, a cross-sectional study of 1103 FN living in twenty-one communities across eight ecozones in BC, Canada, were analysed. Seafood consumption was estimated using a traditional FFQ. Seafood samples were analysed for contaminant contents. RESULTS: Seafood consumption patterns varied significantly across BC ecozones reflecting geographical diversity of seafood species. The top ten most consumed species represented 64 % of total seafood consumption by weight and contributed 69 % to the total EPA+DHA intake. Mean EPA+DHA intake was 660·5 mg/d in males, 404·3 mg/d in females; and 28 % of FN met the Recommended Intake (RI) of 500 mg/d. Salmon was the most preferred species. Seafood consumption was associated with higher fruit and vegetable consumption, lower smoking rate and increased physical activity. Dietary exposure to selected contaminants from seafood was negligible. CONCLUSIONS: In FN in BC, seafood continues to be an essential part of the contemporary diet. Seafood contributed significantly to reaching the RI for EPA+DHA and was associated with a healthier lifestyle. Given numerous health benefits, seafood should be promoted in FN. Efforts towards sustainability of fishing should be directed to maintain and improve access to fisheries for FN.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".