Outcome measurement instruments used to measure diet-related outcomes in infancy: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Supporting positive diet behaviours during infancy is essential to support child health and prevent childhood obesity. How infant diet-related outcomes are measured in trials is crucial to determining intervention effectiveness. This scoping review examined what and how outcome measurement instruments are currently used to measure 13 infant diet-related outcomes from a previously developed core outcome set. METHODS: The databases EMBASE, MEDLINE, CINAHL and PsycINFO were searched from inception to September 2023. Eligible studies reported trials that included infants ≤1 year old and at least one diet-related outcome measurement instrument. Titles/abstracts and full texts were independently screened in duplicate. Data were narratively synthesised. RESULTS: 136 studies reporting 133 trials were included. Outcome measurement instruments used included 66 questionnaires (n = 70 studies), 65 individual questions (n = 45 studies), 24 food diaries/records (n = 21 studies), 11 24-hour dietary recall (n = 11 studies), and healthcare record data (n = 6 studies). Outcome measurement instruments were predominantly self-administered by researchers in participants homes. There was a lack of reporting for some outcome measurements used. CONCLUSION: Review findings highlight the need to improve clarity and completeness of outcome reporting. The findings also provide an important first step to address heterogeneity in measurement of infant diet-related outcomes. Consistent measurement of diet-related outcomes is needed to improve synthesis and evaluation of obesity prevention interventions.
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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 | 0.029 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".