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Enregistrement W7071140548

School nutrition policy adherence and weight status in elementary school children in Prince Edward Island

2013· article· en· W7071140548 sur OpenAlexaboutno aff

Notice bibliographique

RevueIslandScholar (University of Prince Edward Island) · 2013
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueGlobal Maritime and Colonial Histories
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOverweightObesityLogistic regressionHealthy eatingCross-sectional studyChildhood obesity
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,090
Score d'incertitude au seuil0,182

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,226
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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