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Record W2985227232 · doi:10.1093/eurpub/ckz185.286

How to enhance collaboration between primary care and public health?

2019· article· en· W2985227232 on OpenAlexaboutno aff
Bernd Rechel

Bibliographic record

VenueEuropean Journal of Public Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsTimelinePublic healthPublic relationsMEDLINEPrimary carePresentation (obstetrics)Health careMedicineSystematic reviewPolitical scienceFamily medicineNursing

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.439
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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