Inventory on the dietary assessment tools available and needed in africa: a prerequisite for setting up a common methodological research infrastructure for nutritional surveillance, research, and prevention of diet-related non-communicable diseases
Bibliographic record
Abstract
OBJECTIVE: To carry out an inventory on the availability, challenges, and needs of dietary assessment (DA) methods in Africa as a pre-requisite to provide evidence, and set directions (strategies) for implementing common dietary methods and support web-research infrastructure across countries. METHODS: The inventory was performed within the framework of the "Africa's Study on Physical Activity and Dietary Assessment Methods" (AS-PADAM) project. It involves international institutional and African networks. An inventory questionnaire was developed and disseminated through the networks. Eighteen countries responded to the dietary inventory questionnaire. RESULTS: Various DA tools were reported in Africa; 24-Hour Dietary Recall and Food Frequency Questionnaire were the most commonly used tools. Few tools were validated and tested for reliability. Face-to-face interview was the common method of administration. No computerized software or other new (web) technologies were reported. No tools were standardized across countries. CONCLUSIONS: The lack of comparable DA methods across represented countries is a major obstacle to implement comprehensive and joint nutrition-related programmes for surveillance, programme evaluation, research, and prevention. There is a need to develop new or adapt existing DA methods across countries by employing related research infrastructure that has been validated and standardized in other settings, with the view to standardizing methods for wider use.
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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 | 0.041 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".