5.4-O3How have authors from five ethnically diverse countries included and supported ethnicity and race in childhood obesity research?
Bibliographic record
Abstract
Background: Socioeconomic gradients in childhood obesity are well documented. However, differences by ethnicity and race are less well studied. We conducted a systematic review of how studies from Colombia, Brazil, Mexico, Canada and the United Kingdom (UK) have included and supported concepts of ethnicity and race in childhood obesity research. Methods: Published and unpublished cross-sectional and cohort studies, comparing childhood obesity in at least two ethnic groups were reviewed. Studies were identified through Medline, EMBASE, Global Health, WHOLIS and LILACS databases. We undertook a narrative synthesis to analyse definitions of ethnicity and race, and the author’s statement on the reason for using ethnicity as a study variable, the way in which individuals were assigned to ethnicity categories, and the reference to the sources of ethnic codes. Results: After screening 7,436 titles and abstract; 32 were included. Reasons were mentioned in 29 studies, however, in ten studies they were superficial. The way in which individuals were assigned to ethnic codes was stated in 26 studies, using self-report (n = 12), combined or not with parent’s report (n = 9). There were ethnic codes based on family origin (n = 3, UK) and skin colour (n = 2, Brazil). Six studies used Indigenous status previously known as a proxy for ethnicity (3 studies from Mexico, 2 from Brazil, 1 from Canada), one study from Colombia was focused on ethnic minority groups. The majority of studies did not report whether ethnic codes were redefined, nor the source of ethnic categories used or the justification for redefinition. Conclusions: We found that ethnicity and race were included heterogeneously within the studies. There was a lack of arguments supporting definitions, ethnic codes and decisions to handle ethnicity and race. Main message: Policy and interventions to tackle childhood obesity need to be strengthened by more explicit and valid conceptualisation and categorisation of ethnic group status within populations.
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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 | 0.061 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".