Evaluation of the international standardized 24-h dietary recall methodology (GloboDiet) for potential application in research and surveillance within African settings
Bibliographic record
Abstract
BACKGROUND: Collection of reliable and comparable individual food consumption data is of primary importance to better understand, control and monitor malnutrition and its related comorbidities in low- and middle-income countries (LMICs), including in Africa. The lack of standardised dietary tools and their related research support infrastructure remains a major obstacle to implement concerted and region-specific research and action plans worldwide. Citing the magnitude and importance of this challenge, the International Agency for Research on Cancer (IARC/WHO) launched the "Global Nutrition Surveillance initiative" to pilot test the use of a standardized 24-h dietary recall research tool (GloboDiet), validated in Europe, in other regions. In this regard, the development of the GloboDiet-Africa can be optimised by better understanding of the local specific methodological needs, barriers and opportunities. The study aimed to evaluate the standardized 24-h dietary recall research tool (GloboDiet) as a possible common methodology for research and surveillance across Africa. METHODS: A consultative panel of African and international experts in dietary assessment participated in six e-workshop sessions. They completed an in-depth e-questionnaire to evaluate the GloboDiet dietary methodology before and after participating in the e-workshop. RESULTS: The 29 experts expressed their satisfaction on the potential of the software to address local specific needs when evaluating the main structure of the software, the stepwise approach for data collection and standardisation concept. Nevertheless, additional information to better describe local foods and recipes, as well as particular culinary patterns (e.g. mortar pounding), were proposed. Furthermore, food quantification in shared-plates and -bowls eating situations and interviewing of populations with low literacy skills, especially in rural settings, were acknowledged as requiring further specific considerations and appropriate solutions. CONCLUSIONS: An overall positive evaluation of the GloboDiet methodology by both African and international experts, supports the flexibility and potential applicability of this tool in diverse African settings and sets a positive platform for improved dietary monitoring and surveillance. Following this evaluation, prerequisite for future implementation and/or adaptation of GloboDiet in Africa, rigorous and robust capacity building as well as knowledge transfer will be required to roadmap a stepwise approach to implement this methodology across pilot African countries/regions.
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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 | 0.171 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".