Partnerships for public health between regional governments and academic institutions – status quo in European regions
Bibliographic record
Abstract
Abstract The development of partnerships is described as an important tool for achieving goals in the 2030 Agenda for Sustainable Development. Partnerships between regional governments and academic institutions could be important for solving public health issues, as they are often complex, and both academic and practice-related knowledge is necessary. Such partnerships at the regional level might be especially important as many sub-national regions in Europe have considerable responsibility and power in decision-making on important areas such as prevention, health promotion, and health care. The overall aim of this project was to provide more knowledge about public health partnerships between regional governments and academic institutions in different regions within WHO’s Region for Health Network (RHN). Through a mixed methods research design, we wanted to identify and describe good examples of existing partnerships, and to investigate enabling factors, challenges, and lessons learned. Information on ongoing partnerships were collected by sending an electronical questionnaire to contact persons of the 43 members regions in the RHN. Based on the mapping, four regions were selected for group interviews with different stakeholders within regional universities and regional government. There were 31 regions (72%) who answered the questionnaire, and group interviews were carried out in Østfold (Norway), Varna (Bulgaria), Utrecht (the Netherlands), and Saskatchewan (Canada). In this presentation the results from the survey will be presented; describing the occurrence of partnerships, how public health partnerships are formalized and organized, important enablers, hindrances, how collaborations are carried out in practice, perceived benefits and successes, and experienced challenges. Panelists: Klara Dokova Faculty of Public Health, Medical University of Varna, Varna, Bulgaria Contact: klaradokova@gmail.com Liesbeth van Holten Healthy urban living at Provincie Utrecht, Utrecht, Netherlands Contact: liesbeth.van.holten@provincie-utrecht.nl Cordell Neudorf College of Medicine, University of Saskatchewan, Saskatoon, Canada Contact: cory.neudorf@usask.ca Anni Skipstein Chief officer Public Health, Østfold county, Norway Contact: annis@ostfoldfk.no
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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 | 0.052 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".