Factors that Facilitate and Impede the Implementation of Evidence-Informed Chronic Disease Prevention Programs and Policies in Rural Ontario Public Health Units
Notice bibliographique
Résumé
Practitioners, research funders, and policymakers acknowledge the need to implement evidence-informed public health (EIPH) practice to reduce the prevalence of chronic diseases. Although it is difficult to estimate how widely EIPH practices are being applied, several surveys in public health settings demonstrate that, on average, just over half of recommended health practices are implemented. In Canada, people living in rural and remote areas are most vulnerable to chronic diseases. However, the implementation of EIPH practice in rural Ontario public health units (PHUs) is a complex, multidisciplinary process, that occurs within heterogenous and dynamic communities and encompasses different sectors of society. Therefore, this study explores and develops a realist account of the factors that facilitate and impede the implementation of evidence-informed chronic disease prevention (CDP) programs and policies in rural Ontario PHUs (i.e. Rural Public Health Systems – RulPHS). \n \nIntensive, in-depth, semi-structured qualitative interviews and focus groups were conducted in six rural Ontario public health units. Fifteen executives (i.e. CDP Manager/Directors and MOH), participated in the interviews, and 50 public health staff in the area of CDP participated in the focus groups. Interview and focus group data were supplemented by field and reflective notes, and unobtrusive documents provided by the participants. \n \nThe primary method that was used was a qualitative collective case study (multiple), and the study perspective was based on a critical realist ontology. Propositions, sensitizing concepts, and a basic realist model was developed a priori based on extensive research. The Consolidated Framework for Implementation Research (CFIR) was also used to guide the research study. Inductive, deductive, abductive, and retroductive analysis procedures were used to produce a final data structure hierarchy (i.e. categorization scheme). The categorization scheme included five categories, seventeen (17) themes, twenty-one (21) subthemes, and eighty-one (81) factors that facilitated or impeded implementation of CDP programs and policies in rural Ontario PHUs, which were verified through member checks. Solutions were also identified to address barriers to implementation. \n \nFactors that facilitated or impeded implementation were summarized under five broad categories and a further seventeen major themes within them. Major themes were as follows: evidence strength and quality, complexity, adaptability, trialability, cosmopolitanism, external policies and incentives, external leadership engagement, population external communication, reach, population needs and resources, structural characteristics, culture, implementation climate, readiness for implementation, intraorganizational networks and communications, individual identification with organization, and planning. Key lessons learned from the study were also identified. \n \nImplementation was seen to be complex, and there was a plethora of related factors that facilitated and impeded the implementation of evidence-informed CDP programs and policies in Ontario rural PHUs, that occurred over time. These factors closely aligned to many of the factors in the CFIR, which was used to guide this study. Further, critical realism offered insight into the mechanisms (M) with the program and policy, the conditions and contexts (C), under which the generative mechanisms operated, and the patterns of outcomes (O) produced (i.e. realist model). Contributions of rural public health practice, strengths and limitations, and future research were also discussed.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».