Evaluation of dietary assessment instruments in adolescents
Bibliographic record
Abstract
PURPOSE OF REVIEW: The obesity epidemic, the increasing occurrence of adult diseases in childhood, and the growing awareness of a connection between adult diseases and the diet of children and adolescents have led to increased interest in what our youth are eating. Designing an instrument to evaluate adolescents' eating habits requires addressing not only the typical requirements for a diet assessment tool but also the unique concerns of the adolescent population. We reviewed current dietary instruments for adolescents. RECENT FINDINGS: New nutrient assessment methods fall into one of two groups: instruments limited to a specific nutrient/food or those designed for a specific population. The new tools range from Food Intake Recording Software System, a computer program to assist individuals under 10 years of age in reporting their diets, to short food-frequency questionnaires specifically designed to assess fruits and vegetables or fat. Another new instrument uses picture cards to evaluate the entire diet of low-income, overweight African-Americans. The Youth Adolescent Questionnaire, although not a new tool, has been evaluated in new populations (multi-ethnic, multi-income, and multi-education) and with doubly labeled water. SUMMARY: A limited number of dietary assessment instruments that are specifically designed for adolescents have been found to be reproducible and validated. There is a demand for a short, easily administered, inexpensive, accurate instrument that can be used in a broad range of adolescent subpopulations. Future tools will need to meet these criteria and evaluate the 'new' nutrients, foods, and other factors that lead our youth to eat the foods they do.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".