Sustainable public health partnerships: Results from 41 European, Asian and American regions
Bibliographic record
Abstract
Abstract Background Public health problems often constitute so called “wicked problems”, and the importance of involving multiple stakeholders in order to address such problems is acknowledged, for instance through the SDG17 guidelines. Partnerships between academia and the public sector have been deemed especially promising. However, sustainable partnerships might be difficult due to divergent understandings and interests. Although there is a substantial research literature on academic-public partnerships in general, partnerships addressing public health specifically are less investigated. The aim of the project was therefore to identify enablers for sustainable public health partnerships between academia and the public sector. Methods A mixed methods design was used. A survey regarding partnerships was sent to 41 European, Asian and American regions, with a response rate of 72 %. Based on survey data, an interview guide was developed and four best cases (Canada, Bulgaria, the Netherlands and Norway) were identified. Site visits and group interviews with representatives from stakeholders of the partnerships were conducted. Interview data and answers to open ended questions from questionnaires were analysed. Results Three main findings became apparent through the analysis. Important enablers were: 1) person-to-person fit between individuals, 2) national incentive schemes for collaboration, and 3) formal partnership agreements that provided a framework that allowed for manoeuvring. The enablers identified are on a macro, miso and micro level. Furthermore, they can be categorised as political, organisational, and social. Conclusions The data support the notion that partnerships are complex social structures that need to be initiated and managed on different levels and with different measures. At the same time, data demonstrate that across different geographical, political, and social contexts the same enablers are reappearing as important for sustaining public health partnerships. Key messages Similar enablers for sustaining public health partnerships are found across geographical, political, and social contexts. Important enablers for partnerships are person-to-person fit, national incentive schemes, and formal agreements.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".