Collaborative Networks in Chronic Disease Prevention: What Factors Inhibit Partnering for Funding?
Bibliographic record
Abstract
Inter-organisational partnering is seen as an effective mechanism for improving the delivery of chronic disease interventions in communities. Yet even in communities where organisations across multiple sectors are well connected and collaborative in other ways, when it comes to partnering for joint-funding, multiple barriers inhibit the establishment of formal partnerships. To understand why this is so, we examined quantitative and qualitative data from organisations in an Australian community and compared the findings with a review of the published literature in this area. We found that even organisations which are well connected through informal network arrangements face pressure from funding bodies to form more formalised inter-organisational partnerships. Community based organisations also recognise that partnerships are desirable mechanisms for service improvement; however, barriers to joint-funding partnerships exist which include restrictions imposed by funding bodies on the way grants are designed, implemented, and administered. Additional barriers at the community level include organisational capacity for partnership work, intra-organisational restrictions and timing issues. Policy makers must recognise and address the barriers to partnerships which exist within funding structures and at the community level in order to increase partnering opportunities to improve service delivery.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".