Knowledge Translation Practices Of Health Services Research Organizations In The United States
Notice bibliographique
Résumé
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.
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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 | 0,010 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».