Impact of Participative Nutrition Interventions on Iron-Rich Food Consumption in Urban Senegalese Adolescent Girls
Bibliographic record
Abstract
Background: Adolescence is a window of opportunity to rectify nutritional deficiencies.Nonetheless, to improve dietary behaviors and nutritional status of adolescents, it is essential to design sustainable and effective nutrition interventions.Objective: To design, implement and evaluate impact of interventions based on the intervention mapping/ IM model, on iron rich food/IRF consumption among Senegalese adolescent girls.Methods: Experimental design targeting girls aged 13-18 from two colleges in the city of Dakar with one college receiving interventions (EC) and the other serving as a control (CC).Interventions included one nutrition education session, four cooking and recipe sharing sessions and a six-week subsidy to a local vendor for offering affordable IRF.Daily consumption of IRF among adolescents in both colleges, as well as individual and environmental factors that might influence it, were measured before and after interventions.Results: After interventions, the proportion of girls in EC who no longer perceived that consuming IRF caused weight gain increased by about 25% (p=0.001), as did the proportion who felt able to consume at least 85g of IRF/day even if they were not able to prepare IRF themselves (p=0.040).As compared to before interventions (65%), a lower proportion of girls in EC perceived the price of IRF as a barrier to their daily consumption after interventions (24%, p<0.001).In EC, after interventions, the average daily consumption of IRF increased by approximately 25g (p<0.001) while no change was observed in CC (p=0.559). Conclusion:Interventions based on IM targeting individual and environmental factors could be relevant for improving IRF consumption of Senegalese adolescent girls.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".