School nutrition policy adherence and weight status in elementary school children in Prince Edward Island
Bibliographic record
Abstract
The majority of Canadian provinces have adopted school nutrition\npolicies (SNP) in an effort to improve children‟s eating habits and reduce\nchildhood overweight and obesity. While a number of provinces have\nimplemented SNPs, there has been little in terms of evaluation across the\ncountry. All elementary schools in Prince Edward Island (PEI) adopted a SNP in\n2005-2006. The purpose of this study was to describe the changes in SNP\nadherence over time, as well as assess the impact that SNP adherence has on\nchildren‟s overweight and obesity rates. A self-administered survey was\ndistributed to all elementary school principals in 2007 and 2010. The Principal\nSchool Food Survey (Appendix A) consisted of both a subjective and more\nobjective component to assess the level of implementation of all SNP elements.\nThe perceived adherence score was calculated using the responses from 15\nsubjective questions. Food list adherence, the more objective measure of\nadherence, was assessed by comparing the reported food and beverage items sold\nat lunch, in vending machines and canteens to policy guidelines. The relationship\nbetween overweight and obesity rates and both measures of adherence was\nassessed for 2010 only. It was predicted that schools with a higher level of\nadherence would have lower rates of overweight and obesity. Non-parametric\ntests (Wilcoxon rank sum, chi-square and Spearman‟s rho) were used to assess\nchanges in perceived adherence, food list adherence and the agreement between\nfood list and perceived adherence respectively. Logistic regression was used to\nassess the impact that the level of policy adherence had on overweight and\nobesity rates.\nResults indicated that perceived adherence was higher in 2010 than 2007\n(Mann-Whitney U= 519.5, p =0.007). Food list adherence for lunch program\nitems and canteen items decreased significantly from 2007 to 2010 (x2= 12.576,\ndf=3, p=0.006) while vending machines item adherence increased slightly during\nthe same time period (x2=13.689, df=1, p=0.008). There was no significant\nagreement between overall perceived adherence scores and food list adherence;\nhowever, a few policy elements (pricing foods to encourage healthy\nconsumption, promote healthy advertising, serve foods from „most often‟ or\n„sometimes‟ list) did reveal a positive relationship with 2007 food list adherence.\nThere was some support for the hypothesis for the overweight model, in that\ncloser policy adherence (% allowed foods) was associated with lower overweight\nrates in elementary school children. The study also found that schools with\nhigher perceived adherence scores had increased rates of overweight among\ngrade 5 and 6 children. The level of adherence was not, however, a significant\npredictor of obesity rates. These findings are consistent with previous research\ndemonstrating the impact of SNP adherence on overweight rates but not obesity.\nThis study also found that physical activity, breakfast consumption, low-nutrient\ndensity food (LNDF) consumption, student sex and parental education were\nsignificant predictors of both overweight and obesity; in addition to these factors,\nparental income and television frequency were also predictors of obesity. The\nrelationships between the co-variates and overweight and obesity were in the\n3\nexpected direction. While the adoption of a SNP can be a positive first step to\nchange the school food environment, promote healthy eating habits and reduce\noverweight among children, more comprehensive evaluation methods (ie.\nobjectively assessing adherence to all policy elements as opposed to just\navailable food and beverage items) are needed to identify potential barriers to\nimplementation and accurately assess the impact of such policy interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".