What’s happening in the kitchen? The influence of nutritional knowledge, attitudes and, practices (KAP), and kitchen characteristics on women’s dietary quality in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Low dietary quality significantly contributes to public health risks in low-income countries. This situation is particularly concerning for vulnerable groups, such as women and children, who are at increased risk of malnutrition due to inadequate access to proper nutrition. This study aimed to assess the influence of nutrition-related knowledge, attitudes, and practices, and kitchen characteristics on women's dietary quality in Ethiopia. METHOD: A population-based cross-sectional survey was conducted from August to September 2022 in five regions and two city administrations in Ethiopia. A multistage stratified cluster sampling method was employed. From ninety-nine enumeration areas, twenty eligible households were selected. A total of 1,980 women aged 15-49 years were included in this survey. The data were collected using a structured questionnaire about socio-demographic characteristics, food frequency, 24-hour dietary recall, and nutrition-related knowledge, attitudes, and practices. The determinants of dietary quality were identified using Poisson, logistic, and ordinary least square regression analyses. Variables with a p-value less than 0.05 were considered to indicate statistical significance. RESULTS: The results showed that the average dietary diversity score for women was 3.4 ± 0.85. Only 21.5% of the participants achieved the minimum dietary diversity for women (MDD-W), and the mean adequacy ratio for nutrients was 61.6%. The participants' average nutrition-related knowledge, attitudes, and practices scores were 63%, 39%, and 23%, respectively. The regression analysis showed knowledge and attitude positively associated with dietary diversity and the mean nutrient adequacy ratio (P < 0.01). Cooking time and propensity to prepare new food were also positively associated with dietary diversity and with minimum dietary diversity (P < 0.01). CONCLUSION: Our study showed that good nutrition-related knowledge and a positive attitude toward nutrition positively and significantly influence dietary quality, along with cooking time and the propensity to prepare new foods.
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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 | 0.001 |
| 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 | 0.001 | 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".