Developing co-funded multi-sectoral partnerships for chronic disease prevention: a qualitative inquiry into federal governmental public health staff experience
Notice bibliographique
Résumé
BACKGROUND: Multi-sectoral partnerships (MSPs) are frequently cited as a means by which governments can improve population health while leveraging the resources and expertise of the private and non-profit sectors. As part of their efforts in this area, the Public Health Agency of Canada (the Agency) introduced a novel funding programme requiring applicants to procure matched resources from private sources to support large-scale interventions for chronic disease prevention. The current literature on MSPs is limited in its applicability to this model of multi-sectoral engagement. The purpose of this study was to explore the experiences of Agency staff working with potential partners to develop programme applications, such that we might identify lessons from adopting this type of partnership approach. METHODS: Semi-structured interviews were conducted with the 12 staff working in the MSP programme. Interviews were recorded, transcribed and analysed using thematic analysis. Preliminary themes were used to inform follow up focus-groups sessions. A second round of analysis was conducted guided by a coding paradigm focused on understanding process. RESULTS: We identified "experiencing uncertainty" to be a central concept in participants' accounts of the MSP process, related specifically to the MSP programme's novel conditions, shifts that occurred in sectoral roles and demands for new capacities. In response, Agency staff employed strategies to clarify partner interests, build trust in inter-sectoral relationships, and support internal and partner capacity. Outcomes associated with this process include impacts on trust between the Agency and potential partners, a deeper understanding of other sectors, and programme adaptations and refinements to address challenges related to the programme model. CONCLUSIONS: The co-funding model employed by the Agency is a potentially popular one for government bodies wanting to leverage funding from private sector sources. Our study identifies the potential challenges that can occur under this model. Some challenges are related to addressing material conditions related to partner capacity, whereas other challenges speak to deeper and more difficult to address concerns regarding trust and alignment of motivations and interests between partners. Future research exploring the challenges associated with specific models of MSP engagement is necessary to inform approaches to addressing complex problems through collaborative efforts.
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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 | 0,014 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».