A Systematic Review of Tools Measuring Nutrition Knowledge of Pre‐Adolescents and Adolescents in a School‐Based Setting
Bibliographic record
Abstract
BACKGROUND: Measurement of nutrition knowledge is common in interventions targeting dietary modifications in a school-based setting. Previous research has noted a general lack of disclosure regarding the details and psychometric properties of nutrition knowledge tools, which makes uptake of previously used instruments extremely difficult. METHODS: Our systematic literature review sought to identify interventions measuring nutrition knowledge in school settings to students aged 9 to 18. Studies were categorized according to content subject and relevant descriptive characteristics and psychometric properties were extracted. RESULTS: Following the initial screening of 16,868 articles, 308 papers were evaluated for eligibility. Sixty-seven studies consistent with the inclusion criteria were included in the review. A minority of studies reported analysis of validity (31.3%) and/or reliability (40.3%), and 73.1% of studies had at least one unknown relevant descriptive characteristic. The majority (68.7%) of studies were custom developed, of which only 13 reproduced the tool in the publication. CONCLUSION: Overall, there was an alarming lack of reporting across studies, both in terms of the description of knowledge tools as well as their psychometric properties. These omissions make the selection of appropriate instruments for use in novel contexts difficult, and highlight the need for greater disclosure and pre-intervention testing.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".