Development and validation of a tool to assess underlying factors of iron‐rich food consumption among pregnant women
Bibliographic record
Abstract
Anaemia among pregnant women remains a public health concern globally. One major cause of this persistent problem is iron deficiency, which may be the result of limited iron intake in the diet. Using the extended version of the theory of planned behaviour (eTPB), this study aims to develop and validate a questionnaire assessing psychosocial and environmental factors that could influence the consumption of iron-rich foods (IRFs) among Senegalese pregnant women. A three-step procedure was used. Six focus group discussions (FGDs) were held with 10 pregnant women each from a different region to identify salient beliefs related to each of the four constructs of the eTPB using a structured guide. Information from FGDs was used to develop a questionnaire, which was administered to the first group (n = 200) of pregnant women. Principal component analyses and exploratory factorial analyses were performed on the first set of data to identify latent factors for each construct namely the attitude, subjective norm and perceived behavioural control. A revised and shorter version of the questionnaire was administered to a second sample of pregnant women (n = 226) and confirmatory factorial analyses were conducted using this second set of data. Hancock and Muller's H reliability index was computed on the final model. The final questionnaire included 44 items. Most criteria for fit indices were met and H values were satisfactory. This study proposes a tool that could be used to explore determinants of the consumption of IRF among pregnant women. Further validation is still warranted in other contexts.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".