Predicting the Consumption of Iron‐Rich Foods During Pregnancy in Senegal: A Path Analysis
Bibliographic record
Abstract
A limited consumption of iron-rich foods (IRF) is associated with a higher risk of anemia throughout the human life cycle, particularly during pregnancy. Using the extended version of the theory of planned behavior (eTPB), this study aims to identify pathways by which individual (attitude, subjective norm, the perceived behavioral control) and environmental-related factors may influence IRF consumption among pregnant women (PW) from all regions of Senegal. To evaluate IRF consumption in the past day and night, a food frequency questionnaire consisting of a list of IRF with yes/no responses was used. Constructs of the eTPB were assessed through a face-to-face interview conducted with each woman using a valid and reliable questionnaire with Likert scales. Our findings reveal that 9 out of 10 PW (n = 429) had the intention to consume IRF, while about 80% did consume them. Path analyses were conducted. There was no association between the intention and the behavior of interest. The consumption of IRF was predicted by control beliefs or the perceived ability of women to perform the behavior (β = 0.23, p < 0.001). In turn, control beliefs were positively associated with environmental barriers (β = 0.40, p < 0.001). In light of the results, we reiterate the importance of implementing a multisectoral approach to improve the consumption of IRF among PW. Yet, further research is required to better understand pathways through which the intention, psychosocial and environmental factors influence IRF consumption as well as the role of other potential causes of anemia among PW.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".