Cultural Adaptations Addressing Diversity and Health Access in the Mediterranean Diet: A Realist Synthesis
Bibliographic record
Abstract
Background: The Mediterranean diet (MD) has been studied for its benefits, including metabolic risk factors, since the 1950s. In recent years, debates around barriers to access within cultural and environmental fields have arisen within non-Eurocentric cultural backgrounds. Using data related to health benefits derived from dietary components, this review will produce a map of MD modifications to match various cultures. Methods: Foods and constituents of the MD were compared and analyzed to assess benefits for both healthy and metabolic disease states using both empirical and theoretical approaches. Databases (PubMed and Cochrane) were searched using terms for cultural diets and metabolic disease outcomes associated with the MD (e.g., HbA1C, cholesterol, waist circumference, weight, AST and ALT). One multicultural diet database was chosen to identify culturally specific foods that match components of the MD to each cultural affinity. Results: Cultural alternatives to foods and components of the MD exist. However, there is modest research on the specific health effects of most culturally adapted diets. Conclusion: While some evidence gaps exist, it is feasible to translate most components of the MD to diets suitable for various cultural affinities. Future research is needed to examine the overall effects of these diets based on MD macronutrient presentation and the barriers associated with cultural–religious dietary practices and access to foods. Healthcare practitioners may benefit from this as a resource and to facilitate inclusivity and cultural competency for a broader range of dietary behaviours.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".