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Enregistrement W2522695344

Knowledge Translation Practices Of Health Services Research Organizations In The United States

2012· article· en· W2522695344 sur OpenAlexaboutno aff

Notice bibliographique

RevueUND Scholarly Commons (University of North Dakota) · 2012
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Policy Implementation Science
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésKnowledge translationBusinessPublic relationsKnowledge managementPolitical scienceComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Health services research organizations have generated a growing body of literature that focuses on better understanding challenges facing health care delivery. However, their findings do not always reach end users (e.g., policymakers, providers, managers, general public) in ways that are helpful, relevant, or cost-effective despite the availability of numerous resources designed to aid researchers in communicating more effectively. The purpose of this study was to understand better how health services research organizations in the United States communicate their research findings to end users; determine the degree to which they are translating research findings in ways consistent with the empirical evidence; and determine whether organizational characteristics such as university affiliation, organizational specialty, or size explain any variation in responses. Leaders of health services research organizations in the United States responded to a survey about their organizations' knowledge translation practices. The survey instrument and knowledge translation framework were based largely on work conducted by Lavis, Robertson, Woodside, McLeod, and Abelson (2003a) in Canada. Findings from this empirical study expanded the Lavis et al. (2003a) study by setting a baseline for knowledge translation practices, across the research continuum, for health services research organizations in the United States. The data showed that health services research organizations largely communicate about their research in the same manner, regardless of university affiliation, organizational specialty, or size. Research organizations conduct knowledge translation activities throughout the course of their research projects, although in many cases there are gaps between what the literature suggests research organizations optimally should be doing and what they report doing. Notably, these gaps include evaluating knowledge translation activities, utilizing social media tools to extend messaging to end users, engaging with end users throughout the research process, building expectations for knowledge translation into policies and procedures, and investing in knowledge translation development at the organizational level. The findings suggest areas of improvement for health services research organizations. This study observes, however, that increasing knowledge translation capacity will require a cultural shift, and increased collaboration, across the health services research community. Accordingly, this study recommends several action steps. Specifically, health services research organizations should develop knowledge translation expectations through organizational policies and procedures, and invest in capacity building, including training research staff or working with knowledge brokers. Funders should include expectations for knowledge translation in projects, and universities might consider updated promotion and tenure systems that acknowledge and reward translation activities. Bolstering knowledge translation practices as identified in this study, and using the baseline data as a measuring point to evaluate future interventions, contributes to end users successfully receiving research findings in ways that can be useful for decision making, ultimately enhancing the quality of health and health care.

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)0,010
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,137
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,006
Études des sciences et des technologies0,0020,000
Communication savante0,0000,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,632
Tête enseignante GPT0,611
Écart entre enseignants0,021 · 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.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations2
Publié2012
Routes d'admission1
Résumé présentoui

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