Bibliographic record
Abstract
Abstract Background There are almost universal calls for improved collaboration between public health and primary care, but it is less clear how this can be achieved in practice. This presentation summarises key findings from an Observatory policy brief on how to enhance collaboration. Methods The policy brief iss based on a systematic review of the academic literature on collaboration between public health and primary care, searching the databases Medline and Embase for articles published since 2010. After title, abstract and full-text screening, 46 articles were retained and included in the review. Results Most academic articles on collaboration between primary care and public health are concerned with the United States and Canada. From the European countries, the Netherlands and the United Kingdom are most strongly represented. There is also a very uneven timeline in publication, with a spike in articles published in 2012, following an influential Institute of Medicine report. Research has identified organizational models of primary care that are conducive to collaboration with public health, as well as systemic, organizational and interpersonal factors that can facilitate or hinder collaboration. However, the relative importance of these factors remains poorly understood. Improved collaboration between public health and primary care promises to bring major benefits, but these are rarely documented in the literature so far. Furthermore, collaboration may also bring certain risks, such as competition over scarce resources. Conclusions The literature on collaboration between public health and primary care points to many illustrative examples, but also identifies relevant principles and factors that can hinder or facilitate collaboration. Policy interventions to improve collaboration will need to be mindful of potential risks and should aim to demonstrate benefits, which will help to increase buy-in from primary care and public health professionals and the public. Panelists: Ilana Ventura Federal Ministry of Labour, Social Affairs, Health and Consumer Protection, Austrian Government, Vienna, Austria Contact: ilana.ventura@sozialministerium.at Birger Forsberg International Health, Karolinska Institutet, Stockholm, Sweden Contact: Birger.Forsberg@sll.se Rémi Pécault-Charby Caisse Nationale de l’Assurance Maladie, Paris, France Contact: remi.pecault-charby@assurance-maladie.fr
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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 | 0.162 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".