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Record W2771114182 · doi:10.1177/0020731418779954

Social and Economic Policies Matter for Health Equity: Conclusions of the SOPHIE Project

2018· article· en· W2771114182 on OpenAlexaff
Davide Malmusi, Carles Muntaner, Carme Borrell, Marc Suhrcke, Patricia O’Campo, Mireia Julià, Giulia Melis, Laia Palència, Lucia Bosáková, Veronica Toffolutti, Amaia Bacigalupe, Christiane Mitchell, Alix Freiler, Christophe Vanroelen, Gemma Tarafa, Laia Ollé‐Espluga, Esther Sánchez, Lucı́a Artazcoz, Stig Vinberg, Joan Benach, Elena Gelormino, Matteo Tabasso, Anton E. Kunst, Giuseppe Costa, Lluís Camprubí, Fernando Dı́az, María Salvador, Emma Hagqvist, Vanessa Puig‐Barrachina, Glòria Pérez, Dagmar Dzúrová, Andrej Belák

Bibliographic record

VenueInternational Journal of Health Services · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
FundersSeventh Framework Programme
KeywordsHealth equityEquity (law)InequalitySocioeconomic statusImmigrationSocial determinants of healthHealth policySocial equalityEconomic growthSocial policyPolitical sciencePopulationPublic economicsEconomicsSociologyHealth care

Abstract

fetched live from OpenAlex

Since 2011, the SOPHIE project has accumulated evidence regarding the influence of social and economic policies on population health levels, as well as on health inequalities according to socioeconomic position, gender, and immigrant status. Through comparative analyses and evaluation case studies across Europe, SOPHIE has shown how these health inequalities vary according to contexts in macroeconomics, social protection, labor market, built environment, housing, gender equity, and immigrant integration and may be reduced by equity-oriented policies in these fields. These studies can help public health and social justice advocates to build a strong case for fairer social and economic policies that will lead to the reduction of health inequalities that most governments have included among their policy goals. In this article, we summarize the main findings and policy implications of the SOPHIE project and the lessons learned on civil society participation in research and results communication.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.106
GPT teacher head0.532
Teacher spread0.427 · 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 designObservational
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

Citations12
Published2018
Admission routes1
Has abstractyes

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