Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries
Bibliographic record
Abstract
Background Food literacy is capturing the attention worldwide and gaining traction in the Arab countries. Strengthening food and nutrition literacy among Arab teenagers are important promising empowering tools which can protect them from malnutrition. This study aims to assess the nutrition literacy status of adolescents with the food literacy of their parents in 10 Arab countries. Methods This cross-sectional study involving a convenient sample of 5,401 adolescent-parent dyads (adolescents: mean age ± SD: 15.9 ± 3.0, females: 46.8%; parents: mean age ± SD: 45.0 ± 9.1, mothers: 67.8%) was launched between 29 April and 6 June 2022 in 10 Arab nations. The Adolescent Nutrition Literacy Scale (ANLS) and the Short Food Literacy Questionnaire (SFLQ) were used to meet the study aims. Results More than one-quarter (28%) of adolescents had poor nutrition literacy, with 60% of their parents being food illiterate. The top three countries with nutritionally” less literate” adolescents were Qatar (44%), Lebanon (37.4%), and Saudi Arabia (34.9%). Adolescents’ age, gender, education level, primary caregivers, employment status, and the inclusion of nutrition education in the schools’ curriculum predicted the nutrition literacy levels of Arab adolescents. Besides, parental weight status, health status, parent’s food literacy level, and the number of children per household were significant determinants too. Adolescents studying at a university and having parents with adequate food literacy had the highest odds of being nutritionally literate (OR = 4.5, CI = 1.8–11.5, p = 0.001, OR = 1.8, CI = 1.6–2.1, p < 0.001, respectively). Conclusion Nutrition literacy inadequacy among Arab adolescents is a prioritized challenge to be tackled.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".