Systems Thinking and Health Promotion
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Notice bibliographique
Résumé
The launch of the American Journal of Health Promotion blessed the field with a broad conceptual framework, now refined to include physical, emotional, social, spiritual, and intellectual dimensions of health. Throughout the years, this framework has become increasingly nuanced as research and practice have woven the rich fabric of what we know as health promotion today. However, although the multidimensionality of health promotion is firmly established, we still have lacked a shared understanding of the realities of multilevel influence and the value in multilevel intervention. The basic concept is well accepted, as illustrated, for example, by the Stokol’ social ecological model or the Bronfenbrenner developmental ecology model. Recently, there has been growing interest in systems thinking as a framework to guide science and strategy for a more comprehensive, integrated way of addressing individual, group, organization, community, and societal factors that influence health behavior. A serious shift to systems thinking for health promotion would require fundamental reworking of our usual ways of thinking, working, and evaluating. In 2001, the Institute of Medicine (IOM) produced a landmark report called Crossing the Quality Chasm, in which it endorsed the idea that health care systems are complex adaptive systems (CAS). As health promotion shifts to greater attention to multilevel influences and systems change strategy, CAS principles must be considered. The IOM report followed an important publication in 1998 and was accompanied by a series of publications in the British Medical Journal, which advocated the same emphasis on adopting the CAS lens to better understand how to improve and transform health systems. The IOM report was very important, as it was the first high-level consensus report that endorsed the CAS lens. What all of these publications emphasized is the dual nature of CAS: that they are at one and the same time complex and unpredictable, yet amenable to guided transformation by applying simple rules, as long as these rules are applied with the requisite flexibility to allow for adaptation processes. Health promotion issues are increasingly described as complex problems, deeply embedded within the fabric of society; consider, for example, obesity and chronic disease. Complex problems require intervention at many different system levels and the engagement of actors and organizations across levels ranging from the home, school, and work environments to communities, regions, and entire countries. This multi-level, multi-actor view is at the heart of systems thinking. Key features of complex systems that need to be taken into account in health promotion intervention and evaluation include the following: they are self-organizing and constantly adapting to change; they are driven by interactions between systems components and governed by feedback; and they are nonlinear and often unpredictable, with changes on one part of the system producing unexpected changes in other parts. As a consequence of these features, they often are program and policy resistant. This is not the way most of us in health promotion think about the needs and design of our interventions. A fundamental mind shift is needed, as well as major investments in theory, research methods, practice, and policy. Areas of particular importance for further development include interorganizational partnerships, networks, leadership, and integrated strategic communications. To summarize, the health promotion field during the past 25 years has developed a mosaic of research results and intervention strategies, richly colored by a broad, integrative view of key dimension of individual health. What is most needed now is a complementary view of how best to foster and support systems thinking for a more comprehensive, integrated, and dynamic framework for population approaches to health for all.
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.
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 | 0,038 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,004 |
| 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 découle