“Competitive” food and beverage policies and weight status: A systematic review of the evidence among sociodemographic subgroups
Bibliographic record
Abstract
Prior studies identified variable associations between competitive food and beverage policies (CF&B) and youth obesity, potentially due to differences across population subgroups. This review summarizes the evidence on associations between CF&B policies and childhood obesity within gender, grade level/ age, race/ethnicity, and/or socioeconomic levels. PubMed, EMBASE, CINAHL, and ERIC database searches identified studies published in English in Canada and the United States between January 1, 2000, and February 28, 2022. Of the 18 selected studies, six were cross-sectional, two correlational, nine were before/after designs, and one study utilized both a cross-sectional and pre-post design. Twelve studies reported findings stratified by a single sociodemographic factor, with grade level/age as the most frequently reported. Although the evidence varied, greater consistency in direction of associations and strengths of evidence were seen among middle school students. Six studies reported findings jointly by multiple sociodemographic subgroups with evidence suggesting CF&B associations with slower rate of increase or plateaus or declines in obesity among multiple subgroups, though the strengths of evidence varied. Over the past two decades, there have been relatively limited subgroup analyses on studies about CF&B policies and childhood obesity. Studies are needed with stronger designs and analyses disaggregated, particularly by race/ethnicities and socioeconomic factors, across places and time.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".