BMI Measurement in Schools
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: School-based BMI measurement has attracted attention across the nation as a potential approach to address obesity among youth. However, little is known about its impact or effectiveness in changing obesity rates or related physical activity and dietary behaviors that influence obesity. This article describes current BMI-measurement programs and practices, research, and expert recommendations and provides guidance on implementing such an approach. METHODS: An extensive search for scientific articles, position statements, and current state legislation related to BMI-measurement programs was conducted. A literature and policy review was written and presented to a panel of experts. This panel, comprising experts in public health, education, school counseling, school medical care, and parenting, reviewed and provided expertise on this article. RESULTS: School-based BMI-measurement programs are conducted for surveillance or screening purposes. Thirteen states are implementing school-based BMI-measurement programs as required by legislation. Few studies exist that assess the utility of these programs in preventing increases in obesity or the effects these programs may have on weight-related knowledge, attitudes, and behaviors of youth and their families. Typically, expert organizations support school-based BMI surveillance; however, controversy exists over screening. BMI screening does not currently meet all of the American Academy of Pediatrics' criteria for determining whether screening for specific health conditions should be implemented in schools. CONCLUSION: Schools initiating BMI-measurement programs should adhere to safeguards to minimize potential harms and maximize benefits, establish a safe and supportive environment for students of all body sizes, and implement science-based strategies to promote physical activity and healthy eating.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".