Moving Research Translation on Physical Activity to Center Stage
Notice bibliographique
Résumé
The article by Estabrooks and colleagues (1) in this issue of Exercise and Sport Sciences Reviews describes a systematic process for adapting and translating the best evidence concerning physical activity strategies to achieve population level impacts. There is a pressing need to develop approaches of this type, given that the prevalence of physical activity in many countries has remained static or declined in recent years (2–4). Despite an exponential increase in published research output around physical activity, population rates have not improved, suggesting a disconnect between researchers and practice and policy needs (5). The article in this issue presents a model that applies principles similar to those of community-based participatory research to improve the relevance and application of physical activity intervention evidence (1). In other words, taking efficacious programs, identifying the mechanisms of change within these, and working through research-practice partnerships to co-produce solutions at the community level. This involves working with the community, understanding the local context and capacities, and developing programs that meet the real-world feasibility test for implementation. The processes involved rely on local level research-practice partnerships, typically with a single county level agency or organization. These programs require horizontal development out in the community to reach as many sites as possible and vertical integration to include different organizational levels (i.e., deliver staff and managers) engaging in the process. Some of the work described in this article, particularly the Walk Kansas initiative, has reached several thousand people and has been adapted and implemented in other states, such as Virginia and Wyoming, as a modified program called FitEx. This higher level of “scale-up” has substantial national and international relevance. In contrast to the United States, prevention program planning in other countries (such as Canada, Scandinavian countries, Australia, and New Zealand) often is more centrally managed and typically aligned with high-level policy priorities (6,7). This centralized approach requires consultation and partnerships with a myriad of stakeholders across a wide range of settings. Program implementers at local or subregional levels are required to develop, adapt, and implement effective programs, and the focus of evaluation needs to be at the supra-individual or organizational level and make use of complementary qualitative and quantitative methods. One such example was reported in the Estabrooks article (1). In the research-practice partnership model described by Estabrooks (1), a critical phase, once a strategy has been jointly decided upon, is the evaluation of its reach, adoption, and implementation in the context in which it is delivered. Well-developed methods for measuring implementation can be researched using randomized controlled studies, with the degree of implementation being the primary research end point (8,9). Measurements and metrics also differ to those used in efficacy research. For example, partnership affiliation, estimates of adaptation and program fidelity, and estimates of program sustainability become important measures in such evaluations. There also is a need for improvements to the measurement systems, as practitioners often have insufficient time to carry out evaluation measurement tasks. One example of this is in New South Wales, Australia, where a population health information management system for prevention has been developed across regions and collects standardized information across more than 6000 sites, monitors capacity building among the prevention workforce, and monitors uptake of programs across sectors and settings (10). This kind of information system centralizes and institutionalizes the collection of information from regions or communities and can be used to facilitate widescale project evaluation and between-project comparison. These tools and methods are unlikely to be developed by researchers alone, and partnerships with practitioners at the community level and policymakers at the broader regional level are an important next step to co-create optimal evaluation approaches. In many national contexts, this collaborative way of working in program evaluation and implementation represents a transformative change that will only be realized through supportive values, governance, resourcing, and structures. This work is in its infancy but is essential to understanding the processes of physical activity programming at the population level, with the capacity to refine and improve them. As the Lancet 2012 physical activity series said, “More of the same is not enough”; a maxim that remains highly relevant for our current programmatic and evaluation practice in community-wide physical activity programs (11). Adrian E. BaumanBen J. SmithWilliam BellewSchool of Public Health Sydney University Australia
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».