Obesity Prevention in a City State: Lessons from New York City during the Bloomberg Administration
Notice bibliographique
Résumé
OBJECTIVE: To illuminate the key components of multi-sector reform to address the obesogenic environment in New York City during the administration of Mayor Michael Bloomberg from 2002 to 2013, we conducted a case study consisting of interviews with and a critical analysis of the experiences of leading decision makers and implementers. METHOD: Key informant interviews (N = 41) conducted in 2014 were recorded, transcribed, coded, and thematically analyzed. Participants included officials from the Health Department and other New York City Government agencies, academics, civil society members, and private sector executives. RESULTS: Participants described Mayor Bloomberg as a data-driven politician who wanted to improve the lives of New Yorkers. He appointed talented Commissioners and encouraged them and their staff to be bold, innovative, and collaborative. Multiple programs spanning multiple sectors, with varied approaches and targets, were supported. This study found that much of the work relied on loose coalitions across City Government, with single agencies responsible for their own agendas, some with health co-benefits. Many policies were implemented through non-legislative mechanisms such as executive orders and the Health Code. Despite support from academic and some civil society groups, strong lobbying from industry and an unfavorable media led to some reforms being modified, legally challenged or blocked completely, particularly food environment modifiers. In contrast, reforms of the physical environment were described as highly consultative across and outside government and resulted in slower but more sustained reform. CONCLUSION: The Bloomberg administration was a "window of opportunity" with the imprimatur of the executive to progress a long-term, multi-faceted obesity prevention strategy, which has successfully reversed childhood trends. Through the involvement of external researchers and the extensive use of empirical data from a wide range of participants, this study offers a unique insight into the ways in which this was achieved. While some of the aspects of the reforms in New York City are unique to that setting at that time, there are important lessons that are transferable to other urban settings. These include: strong and consistent leadership; a commitment to innovative approaches and cross-sectoral collaboration; and a context to support and encourage this approach.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».