Prevalence of micronutrient inadequacy differs by severity of food insecurity among adults living in Canada in 2015
Bibliographic record
Abstract
Household food insecurity is generally associated with poorer quality diets in Canada, but whether household food insecurity heightens the probability of inadequate micronutrient intakes is unknown. The objective of this research was to investigate how prevalence of micronutrient inadequacy differed by severity of household food insecurity among adults in Canada. Using the 2015 Canadian Community Health Survey Nutrition, this study included participants aged 19–64 years who completed up to two 24 h dietary recalls and provided details about household food insecurity ( n = 9486). Children and older adults were not included due to sample size limitations. Usual micronutrient intake distributions were estimated by a four-level measure of food insecurity status using the National Cancer Institute method. Welch’s t tests assessed differences in prevalence of inadequacy for selected micronutrients. Prevalence differed for some micronutrients among those living in marginally and moderately food insecure compared to food-secure households. The greatest differences in prevalence of inadequacy were observed between severely food-insecure and food-secure households: vitamin A (60.0%, SE = 11.9 vs. 40.6%, SE = 2.7, p < 0.0001), vitamin B6 (42.7%, SE = 9.1 vs. 12.8%, SE = 2.5, p < 0.0001), folate (39.4%, SE = 10.0 vs. 15.9%, SE = 2.2, p < 0.0001), vitamin C (63.3%, SE = 5.2 vs. 29.1%, SE = 2.8, p < 0.0001), calcium (78.6%, SE = 6.4 vs. 58.7%, SE = 1.3, p < 0.0001), magnesium (75.6%, SE = 9.5 vs. 48.7%, SE = 1.2, p < 0.0001), and zinc (34.9%, SE = 10.0 vs. 23.2%, SE = 2.4, p = 0.0009). Apparent underreporting also differed by severity of food insecurity, with increased underreporting observed with worsening food insecurity. The probability of inadequate micronutrient intakes among adults rises sharply with more severe household food insecurity in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".