Dietary glycaemic index and glycaemic load among Australian children and adolescents: results from the 2011–2012 Australian Health Survey
Bibliographic record
Abstract
This study aimed to examine the dietary glycaemic index (GI) and glycaemic load (GL) of Australian children and adolescents, as well as the major food groups contributing to GL, in the recent 2011-2012 Australian Health Survey. Plausible food intake data from 1876 children and adolescents (51 % boys), collected using a multiple-pass 24-h recall, were analysed. The GI of foods was assigned based on a step-wise published method using values from common GI databases. Descriptive statistics were calculated for dietary GI, GL and contribution to GL by food groups, stratified by age group and sex. Linear regression was used to test for trends across age groups for BMI, dietary GI and GL, and intakes of energy, nutrients and food groups. Pearson's χ 2 test was used to test for differences between age groups for categorical subject characteristic variables. Mean dietary GI and GL of participants were 55·5 (sd 5·3) and 137·4 (sd 50·8), respectively. The main contributors to dietary GL were starchy foods: breads, cereal-based dishes, breakfast cereals, flours, grains and potatoes accounted for 41 % of total GL. Sweetened beverages, fruit and vegetable juices/drinks, cake-type desserts and sweet biscuits contributed 15 %. No significant difference (at P<0·001) was observed between sexes. In conclusion, Australian children and adolescents appear to consume diets with a lower GI than European children. Exchanging high-GI foods for low-GI alternatives within core and non-core foods may improve diet quality of Australian children and adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".