Growth measures among preschool-age Inuit children living in Canada and Greenland
Bibliographic record
Abstract
AIM: The present study reports findings from a study of preschool-age Inuit children living in the Arctic regions of Canada and Greenland. METHODS: We compare stature and obesity measures using cutoffs from the Centers for Disease Control and the International Obesity Task Force references. The sample is comprised of 1121 Inuit children (554 boys and 567 girls) aged 3-5 years living in Nunavut (n=376) and Nunavik (n=87), Canada, in the capital city of Nuuk, Greenland (n=86), and in Greenland's remaining towns and villages (n=572). RESULTS: Greenland Inuit children were significantly taller than their Canadian counterparts, with greatest height and weight observed among children from Nuuk. Overall prevalence of stunting was low with the three cutoffs yielding similar values for height-for-age z-scores. Obesity prevalence was higher among Canadian Inuit children than their Greenland counterparts. CONCLUSIONS: Inuit children have stature values consistent with those of the Centers for Disease Control reference and low prevalence of stunting, though geographic variability in mean stature values between Canadian and Greenlandic samples likely reflects differences in both socioeconomic status and genetic admixture. Obesity prevalence is high among both Canadian and Greenland Inuit preschoolers, with children living in the city of Nuuk exhibiting lower obesity prevalence than children living in either Nunavut or Nunavik, Canada or Greenland's towns and villages. Varying obesity prevalence may reflect varying degrees of food security in remote locations as well as the influence of stature and sitting height which have not been well studied in young Inuit children.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".