Developing co-funded multi-sectoral partnerships for chronic disease prevention: a qualitative inquiry into federal governmental public health staff experience
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".