Gaps and opportunities for nutrition research in relation to non-communicable diseases in Arab countries: Call for an informed research agenda
Bibliographic record
Abstract
Effective public health nutrition interventions are needed to curb the escalating prevalence of non-communicable diseases (NCDs) in many Arab countries. In order to generate the scientific evidence needed for the success of these interventions, an informed research agenda should be developed. The purpose of this review is to identify gaps and opportunities for research on nutrition and NCDs among Arab countries, which is an important step towards the formulation of this research agenda. Published papers that addressed nutrition and NCDs in Arab countries between the years 2006 and 2015 were reviewed (n=824). The main gaps identified were related to the predominance of laboratory-based studies with few cohort and intervention studies, and the small percentage of articles examining dietary patterns. While food frequency questionnaires were the main dietary assessment method used, only 35% were validated. Very few studies included children and the majority considered nutrition in isolation, excluding other environmental factors. Opportunities identified included the promising momentum in studying nutrition and NCDs among Arab countries, evidenced by an increasing number of articles published over the years, that may be guided in future nutrition research to fill the identified gaps. In addition, the higher number of articles in high-income countries coupled with the impact of papers in middle-income countries suggests an opportunity of synergistic collaboration among these countries. The identified gaps and opportunities in this review may serve as basis for Arab countries to start developing a research agenda in the area of nutrition and NCDs.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".