Developing an online food composition database for an Indigenous population in south-western Uganda
Bibliographic record
Abstract
OBJECTIVE: To develop an online food composition database of locally consumed foods among an Indigenous population in south-western Uganda. DESIGN: Using a community-based approach and collaboration with local nutritionists, we collected a list of foods for inclusion in the database through focus group discussions, an individual dietary survey and markets and shops assessment. The food database was then created using seven steps: identification of foods for inclusion in the database; initial data cleaning and removal of duplicate items; linkage of foods to existing generic food composition tables; mapping and calculation of the nutrient content of recipes and foods; allocating portion sizes and accompanying foods; quality checks with local and international nutritionists; and translation into relevant local languages. SETTING: Kanungu District, south-western Uganda. PARTICIPANTS: Seventy-four participants, 36 Indigenous Batwa and 38 Bakiga, were randomly selected and interviewed to inform the development of a food list prior the construction of the food database. RESULTS: We developed an online food database for south-western Uganda including 148 commonly consumed foods complete with values for 120 micronutrients and macronutrients. This was for use with the online dietary assessment tool myfood24. Of the locally reported foods included, 56 % (n 82 items) of the items were already available in the myfood24 database, while 25 % (n 37 items) were found in existing Ugandan and Tanzanian food databases, 18 % (n 27 items) came from generated recipes and 1 % (n 2 items) from food packaging labels. CONCLUSION: Locally relevant food databases are sparse for African Indigenous communities. Here, we created a tool that can be used for assessing food intake and for tracking undernutrition among the communities living in Kanungu District. This will help to develop locally relevant food and nutrition policies.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".