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Record W3092346941 · doi:10.1093/eurpub/ckaa165.116

2.I. Workshop: Implementing local health equity policies in Europe – Needs, governance and tools

2020· article· en· W3092346941 on OpenAlexaboutno aff

Bibliographic record

VenueEuropean Journal of Public Health · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsHealth policyEquity (law)Context (archaeology)Government (linguistics)Corporate governanceInequalityHealth equityPolitical scienceBusinessPublic relationsEconomic growthHealth careEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract The effects of health inequalities within and between European countries are widely recognized, and reducing health inequalities is on the agenda of many countries. Despite an increasing concern and awareness on health inequalities, a wide gap exists in Europe in terms of political response. Health is created and lived by people within the settings of their everyday life; where they learn, work, play and love. Healthy urban development has a great potential to reduce health inequalities. Healthy living environments can only be created if sectors other than the health sector are involved. Health in all policies (HiAP) is an approach promoted by WHO since the Ottawa Charta (1986). It acknowledges the need for an integrated approach to health involving different policy fields. The reduction of health inequalities is one core aim. Including HiAP is a smart - and feasible - policy choice and one concrete measure it to use prospective Health Impact Assessment focusing on equity. Working with other government sectors requires an understanding of different mandates and goals, and may involve crossing administrative and budgetary barriers between sectors. Different policy actors and professional disciplines have their own languages and approaches to the problems and opportunities in societal development. For this reason, HiAP needs to promote an understanding of the language, goals and working methods across government sectors. Municipal governments need to build trusting and collaborative relationships both between internal sector silos, and across stakeholders within society. The municipal context offers comprehensive entry points for action. Municipalities seek to provide education throughout the life course, create appropriate conditions for housing as well as for physical activity and healthy eating. Municipalities can also promote the creation of a stable ecosystem. Moreover, a focus on municipalities addresses the local political context, local political regulations and urban or rural planning and development, which are important contributions to improving living conditions. There is valid information on health, health inequalities and its determinants available, but the information is not automatically transformed to concrete policy actions and measures. Besides knowledge, policy implementation requires many other elements to be effective: political will and commitment, collaboration, resources and governance. This session presents current findings and actions in the frame of the EU Joint Action Health Equity Europe (JAHEE). The first contribution includes an analysis of specific governance aspects for healthy living environments that are being addressed in JAHEE: How is the process from needs to decision-making to actions done by the participating 13 countries? After that, 4 examples from the Netherlands, Italy and Spain will describe their needs, governance and tools while implementing local health equity policies in their own context. Key messages The local level is the place where many determinants of health can be shaped and where Health and Equity in all Policies can be realized in an innovative way. There are many existing examples for tools and governance for local health equity policies that can be transferred to other places.

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0020.009
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0210.004

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 head0.173
GPT teacher head0.360
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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