Assessment of mothers’ knowledge and practice towards aflatoxin contamination in complementary foods in Ethiopia: from pre-harvest to household
Bibliographic record
Abstract
This study assessed the knowledge and practices of the mothers in Ethiopia on issues related to aflatoxin in complementary foods (CFs) using structured questionnaires. The study involved 195 mothers from Amhara, Tigray, Oromia, and Southern Nations Nationalities and Peoples (SNNP) regions and addressed a range of issues used to prevent the risks of mould and aflatoxin contamination from farm-to-table. Of the 195 mothers who responded; 186 (95%) were ploughing their land before growing the next crop, 177 (91%) used the crop rotation schedule, 157 (81%) reported the practice of removing old seed heads and stalks used as an inoculum for aflatoxin contamination, 185 (95%) harvested the crops as soon as the crops were matured, 138 (70%) used a threshing method known as ‘trampling by hoofed animals’. After threshing, about 124 (64%) respondents had the knowledge and practice of drying cereals and legumes to decrease the moisture content, 134 (68%) used solar drying on a bare ground, 184 (94%) cleaned and disinfected the storage structures before storage. Almost all the respondents practiced the CFs processing steps properly and they used colour, type, odour, insect infestations, mouldiness and all these criteria to select the CFs ingredients. A total of 78 (40%) respondents had the practice of feeding mouldy cereals and legumes to animals, and 89 (46%) of the respondents erroneously believed that roasting can decontaminate the aflatoxin produced from mouldy cereals. As a general conclusion, the majority of the respondents are practicing good agricultural practices in the field, but they use poor storage and processing practices at homes which are susceptible to mould and aflatoxin contamination. Besides, they lack awareness about the toxic effects of aflatoxin on human health. Therefore, awareness creation about the health impacts of aflatoxin and methods of prevention need to be implemented in the study areas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".