Nutritional Challenges and Dietary Practices of Ethnic Minority (Indigenous) Groups in China: A Critical Appraisal
Bibliographic record
Abstract
Indigenous food systems can affect multiple aspects of Indigenous people's health. In China, the government declared that there are no Indigenous people in China and used the term “ethnic minority groups” instead. However, to date, no attempt has been made to investigate the nutrition status and dietary practices of all 55 ethnic minority groups. To understand this pertinent issue, a systematic review is required. The main selection criteria were publications should be about nutrition status or dietary practices among ethnic minority groups in China, specify the name of the ethnic minority group, and be published within the past 10 years. For this literature review, 111 publications were selected through Wanfang Med Online for Chinese publications and Google Scholar for English publications. Linear regressions were applied to explore what factors can affect the total number of publications for an ethnic minority group. The main findings include that only 15 ethnic minority groups have dietary intake data representing the general people of the ethnic group; only seven ethnic minority groups have data for both nutrition status (anthropometric and nutrients intake/deficiency) and dietary practices (dietary intake and dietary habits); there are still 10 ethnic minority groups with a total number of population 845,420 that lack studies on both nutrition status and dietary practices; ethnic minority groups are suffering from double-burden malnutrition and consuming unbalanced diets; primary and middle school students are the most prevalent study population than any other age group due to easy access; and an ethnic minority group is likely to have more publications about nutrition status and dietary practices if they have a larger population or are unique to a region. The results indicate that more national-level programs and timely nutrition and dietary reports should be implemented to address double-burden malnutrition and unbalanced diets among ethnic minority groups in China. More studies involving maternal nutrition, targeting underrepresented ethnic minority groups and age groups, and exploring traditional food systems in China are also essential to better understand and address this issue.
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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 | 0.030 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".