Feasibility and acceptability of food‐based complementary feeding recommendations using Trials of Improved Practices among poor families in rural Eastern and Western Uganda
Bibliographic record
Abstract
Inadequate complementary feeding practices are a major contributor to stunting among children in Uganda. The WHO recommends the promotion of local food-based complementary feeding recommendations (FBCFRs) to address nutrient gaps during complementary feeding. This study tested the feasibility and acceptability of FBCFRs, using trials of improved practices (TIPs). Qualitative and quantitative methods were used in a cross-sectional survey over three household visits. At first household visit, information on socio-demographic factors and food frequency was collected and FBCFRs introduced. The second household visit assessed the use and barriers related to the FBCFRs, while the third household visit assessed the continued use of the FBCFRs. Focus group discussions and key informant interviews provided the insights into community norms on the FBCFRs. Most FBCFRs were feasible and acceptable. However, caretakers found it difficult to implement a full set of FBCFRs together with the recommended frequencies. Caretakers were more likely to try and continue using FBCFRs that had familiar methods of preparation and commonly used ingredients. Seasonality and cost were major barriers to use. Through TIPs, mothers demonstrated that they are open to try new ways of improving their children's nutrition.
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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 | 0.037 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".