Characteristics of Global Data on Adolescent’s Dietary Intake: A Systematic Scoping Review
Bibliographic record
Abstract
Data on adolescents' dietary intake are essential to improve their diets and nutrition. However, the availability of (high-quality) data on adolescents' dietary intake is scarce with great global differences. We conducted a systematic scoping review to investigate the availability, characteristics, and gaps in global adolescent dietary data, following the Preferred Reporting Items for Systematic Reviews and Meta Analyses-Extension for Scoping Reviews checklist and guidelines (registered under PROSPERO no. 171170 https://www.crd.york.ac.uk/PROSPERO/). We included peer-reviewed and grey literature articles (2010 onwards) on the dietary intake of male and female adolescents (10-24 y). Studies from all countries and languages and including any information related to types of food consumed, diet composition, dietary diversity, or meal patterns were considered. We excluded studies with insufficient methodological information, unclear description of population, samples sizes <25, school-based data sets containing <6 schools, and studies that focused on pregnant or unhealthy study populations. Data, including year(s) of data collection, age, gender, sample size, dietary assessment methods, number of food items/groups, study design, location, and representativeness, were extracted. A total of 52,889 titles were identified and 722 articles, describing 1,322 data sets, were retained for analysis. Nationally representative, detailed dietary data for adolescents aged 10-24 y are still lacking, particularly in sub-Saharan Africa, South Asia, and low-income countries. Data quality and representativeness remain limited, highlighting the need for data disaggregation by age, gender, locality, comprehensive dietary information, and broader geographic coverage. A notable amount of data was available through grey literature, especially in data-scarce countries. The study underscores the importance of addressing adolescent nutrition, emphasizing the urgent need for more robust, accessible, and representative data on adolescents' dietary intake to support effective nutritional efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.035 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".