Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mary Gatineau and Shireen Mathrani from the National Obesity Observatory explore the relationship between ethnicity and obesity in the UK There is no straightforward relationship between obesity and ethnicity. Obesity prevalence varies substantially between ethnic groups in the UK and interpretation of data is difficult because of uncertainty about appropriate obesity thresholds and associated levels of health risk. In addition, health behaviours both across and within minority ethnic groups are influenced by a complex interplay of cultural, lifestyle and socioeconomic factors.1 Obesity prevalence The most current data on adult obesity by ethnic group are from the Health Survey for England (HSE) 2004. Findings suggest that compared to the general population, obesity prevalence is lower among men from black African, Indian, Pakistani and most markedly, Bangladeshi and Chinese communities. Among women, obesity prevalence appears to be higher for those from Black African, Black Caribbean and Pakistani groups than for women in the general population and lower for women from the Chinese ethnic group.2 The National Child Measurement Programme (NCMP) provides the most robust data on child obesity in the UK and includes a detailed breakdown by ethnic sub-group. Recent analysis by the National Obesity Observatory (NOO)i shows that in Reception class, obesity prevalence is especially high for boys and girls from Black African and Black other ethnic groups and boys from the Bangladeshi ethnic group.ii The pattern for girls in Year 6 is broadly similar to that of girls in Reception, while for boys in Year 6, obesity prevalence is significantly higher for all ethnic groups compared to White British, with boys of Bangladeshi ethnicity having the highest prevalence. The analysis also finds a trend of rising obesity prevalence for both boys and girls of Bangladeshi ethnicity, with no significant changes in any other ethnic groups.3 Figure 1 provides a summary of this rising trend for Bangladeshi children in Year 6 compared to all other ethnic groups combined. Obesity measures and thresholds There are a number of issues associated with the measurement of obesity and the thresholds used for minority ethnic groups in the UK. Different ethnic groups are associated with a range of different body shapes and different physiological responses to fat storage. Body mass index (BMI) is not always an accurate predictor of body fat or fat distribution in individuals. Research has shown that for the same level of BMI, people of African ethnicity appear likely to carry less fat and people of South Asian ethnicity more fat than the general population. This may have led to an overestimation of obesity among African and an underestimation among South Asian groups.4 South Asian and Chinese populations have been found to be at risk of chronic diseases and mortality at lower levels than European populations. Revised BMI thresholds and waist circumference measures have been recommended for these groups. NCMP findings demonstrate a very high prevalence of obesity among boys of Bangladeshi ethnicity. These findings are in contrast with the general perception that children from Black ethnic groups have the highest obesity prevalence. The high odds of children from Black groups being classified as obese may in fact be due to physical characteristics related to ethnicity and, in particular, height, which can lead to skewed BMI.3,iii Factors determining obesity riskiv Dietary patterns of minority ethnic groups are influenced by many factors including availability of food, level of income, health, food beliefs, religion, cultural patterns and customs.5 While many people from these groups have healthier eating patterns than the White population, less healthy diets are known to be of concern in some groups, in particular those of South Asian origin. Migration to the UK has a significant impact on dietary habits. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it