Information-Seeking Behaviors and Other Factors Contributing to Successful Implementation of Evidence-Based Practices in Local Health Departments
Notice bibliographique
Résumé
In Brief The objective of this article was to describe factors that contribute to successful translation of science into evidence-based practices and their implementation in public health practice agencies, based on a review of the literature and evidence from a series of case studies. The case studies involved structured interviews with key informants in 4 health departments and with 4 corresponding partners from academic institutions. Interviews were recorded and transcribed, coded by 2 independent, trained coders, using a standard codebook. A thematic analysis of codes was conducted. Coding was entered into Atlas TI software for further analysis. Results from the literature review indicated that only approximately half of programs implemented in state and local health departments were evidence based. Lack of time, inadequate funding, and absence of cultural and managerial support—including incentives—are among the most commonly cited barriers to implementing evidence-based practices. Findings from the case studies suggest that these health departments, successful in implementing evidence-based practices, have strong relationships and good communication channels established with their academic partner(s). There is strong leadership engagement from within the health department and in the academic institution. Implementation of evidence-based programs was most often related to high priority community needs and the availability of resources to address these needs. The practice agencies operate with a culture of quality improvement throughout the agency. Information technology, training, how the interventions are bundled, including their complexity and ability to be customized and resource requirements are all fruitful avenues for further research. This article describes factors that contribute to successful implementation of public health science. Health departments that are successful in implementing evidence-based practices have strong relationships and good communication channels established with their academic partner(s). Implementation of evidence-based programs was most often related to high priority community needs and the availability of resources to address these needs.
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 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 | 0,054 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,005 |
| 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 | 0,000 |
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 ».