Measuring and Predicting Intention of Senegalese Mothers to Provide Iron-Rich Foods to their Children
Bibliographic record
Abstract
In Senegal, only 43% of children aged 6-23 months are provided with iron-rich foods (IRF). Assessing determinants of mothers’ behaviour is imperative to improve young children nutrition. We developed a validated questionnaire and used it to assess psychosocial factors of mothers' intention to provide IRF to their children aged 6-23 months in the Matam area, Senegal. Using the planned behaviour theory, the first version of a questionnaire was developed and administered to 120 mothers. Exploratory factorial analyses (EFA) were used to generate a shorter and validated final version of the questionnaire, administered to another sample (N=100) of mothers to assess psychosocial factors underlying their intention to provide IRF to children. EFA revealed the importance of perceived benefits mothers have for the health/welfare of their children if providing them IRF, about acting according to expectations of persons in their surroundings and limited access to IRF due to physical/financial constraints. Attitude (β=0.26, p = 0.015) and subjective norm (β = 0.22, p = 0.047) were positively associated with mothers’ intention to provide IRF to their children. Together with sociodemographic variables, they explained 14% of its variance. Our findings revealed that mothers have a strong intention to provide IRF to their children. Although further assessments of this questionnaire are warranted in other contexts, this tool could provide information on potential determinants of mothers’ intention to provide IRF to children.
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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 | 0.003 |
| 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) | 0.001 | 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".