Review of Body Mass Index Reduction Interventions among Mexican Origin Latinos and Latinas
Bibliographic record
Abstract
Objectives: A literature review was conducted to identify factors associated with successful Body Mass Index (BMI) reduction interventions for Mexican origin US Hispanic/Latino populations. Data Source: An academic database search was conducted of peer-reviewed literature primarily in public health, medical anthropology, medical sociology, and biomedical databases. The key search words used were “Latino or Hispanic or Mexican”, in combination with “intervention”, “obesity”, “body mass index”, “weight reduction”, “best practices” and “lessons learned”. Inclusion Criteria: The inclusion criteria included an intervention protocol, with BMI measures, and a majority of participants identified as Mexican origin Hispanics. Search results yielded a total of 118 articles with 19 studies meeting the inclusion criteria. Results: The review found that education and the use of culturally tailored/sensitive materials are important factors in BMI reduction. In addition, the study found that family centered and community based approaches are some of the most successful evidence based practices found in the Latino health literature. Conclusions: Obesity and its sequelae disproportionately impact both US and non-US Latino/ Hispanic communities and have life-long and intergenerational consequences. The findings from this review may serve as a guide to the development of more successful interventions and best practices to address the needs of Mexican origin Latino 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".