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Enregistrement W2973025941 · doi:10.1249/jes.0000000000000195

Moving Research Translation on Physical Activity to Center Stage

2019· review· en· W2973025941 sur OpenAlexaboutno aff
Adrian Bauman, Ben J. Smith, William Bellew

Notice bibliographique

RevueExercise and Sport Sciences Reviews · 2019
Typereview
Langueen
DomaineMedicine
ThématiquePhysical Activity and Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Public relationsPopulationAgency (philosophy)Knowledge translationProcess (computing)Intervention (counseling)Relevance (law)Work (physics)Participatory action researchPsychologyPolitical scienceSociologyKnowledge managementMedicineComputer scienceEngineeringSocial scienceGeographyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

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

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,980
Score d'incertitude au seuil0,785

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0030,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,478
Tête enseignante GPT0,539
Écart entre enseignants0,061 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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