A systems-oriented multilevel framework for addressing obesity in the 21st century.
Notice bibliographique
Résumé
Effective or sustainable prevention strategies for obesity, particularly in youths, have been elusive since the recognition of obesity as a major public health issue 2 decades ago. Although many advances have been made with regard to the basic biology of adiposity and behavioral modifications at the individual level, little success has been achieved in either preventing further weight gain or maintaining weight loss on a population level (1). To a great extent, this is the result of the complex task of trying to change the way people eat, move, and live, and sustaining those changes over time. The most immediate cause of obesity is an imbalance of energy intake and energy expenditure in the body. This energy imbalance, on the magnitude seen in today's population, arises from the complex interactions of biological susceptibilities and socioenvironmental changes (2). Evidence in behavioral economics suggests that these powerful biological and contextual forces often place eating and exercise behavior beyond an individual's rational control (3). Therefore, the solution to the obesity epidemic lies in policies and interventions that alter those contextual features, taking individual biology and preferences into account. Historically, obesity research has been conducted within individual disciplines. Now, for both scientific inquiry and for public policies, obesity should be framed as a complex system in which behavior is affected by multiple individual-level factors and socioenvironmental factors (ie, factors related to the food, physical, cultural, or economic environment that enable or constrain human behavior, or both). These factors are heterogeneous and interdependent, and they interact dynamically (4). Because of the complex system that affects obesity, researchers need to use a systems-oriented approach to address the multiple factors and levels. Whereas multidisciplinary research consists of teams with different expertise that can contribute to the understanding of particular aspects of a larger research question, truly cross-disciplinary research asks a priori questions and poses hypotheses that cut across disciplines and across levels of influence. For example, how do biological mechanisms of energy metabolism react to or how are they affected by different features of the built, social, or economic environment to produce a given distribution of eating or physical activity? How do these conditions enable or constrain eating and physical activity, and how are they embodied in biological systems to affect these behaviors? In October 2007, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) convened the international conference Beyond Individual Behavior: Multidimensional Research in Obesity Linking Biology to Society. The goal was to create a climate of training, funding, and academic and institutional support for obesity research that will offer sustainable solutions to the obesity problem. Participants hoped to bridge the factors that influence obesity-related behaviors at the macro level (typically policies that shape and govern the food, physical, social, and economic environments in which we live) and the micro level (typically variables within people or their immediate surroundings that influence health outcomes). The conference was supported by the National Institutes of Health (National Cancer Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Division of Nutrition Research Coordination, Office of Behavioral and Social Sciences Research; and Office of Disease Prevention), the Canadian Institutes of Health Research (Institute of Nutrition, Metabolism, and Diabetes), and the Centers for Disease Control and Prevention. The content of this 3-day conference was designed to explicate the scientific foundation of this multilevel approach, generate research questions that apply to all disciplines, consider different intervention models, and discuss methods needed for the design and analysis of systems-oriented, multilevel studies (5). The essential elements of this multilevel agenda are framing obesity as a complex systems problem; encouraging cross-disciplinary questions and hypotheses; focusing on structural interventions (ie, modifications to the environment or policies); building capacity for multilevel research and action; and taking a global perspective.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,013 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».