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Record W2691478068 · doi:10.1177/0020731417712509

Retrenched Welfare Regimes Still Lessen Social Class Inequalities in Health

2017· article· en· W2691478068 on OpenAlexaff
Carles Muntañer, Owen Davis, K. McIsaack, Lauri Kokkinen, Ketan Shankardass, Patricia O’Campo

Bibliographic record

VenueInternational Journal of Health Services · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsWilfrid Laurier UniversitySt. Michael's HospitalNova Scotia Health AuthorityPublic Health OntarioUniversity of Toronto
FundersEconomic and Social Research Council
KeywordsWelfareInequalitySocial classDemographic economicsGlobalizationSocial inequalityNeoliberalism (international relations)Social WelfareDevelopment economicsSociologyPolitical scienceEconomicsPolitical economy

Abstract

fetched live from OpenAlex

This article builds on recent work that has explored how welfare regimes moderate social class inequalities in health. It extends research to date by using longitudinal data from the EU-SILC (2003-2010) and examines how the relationship between social class and self-reported health and chronic conditions varies across 23 countries, which are split into five welfare regimes (Nordic, Anglo-Saxon, Eastern, Southern, and Continental). Our analysis finds that health across all classes was only worse in Eastern Europe (compared with the Nordic countries). In contrast, we find evidence that the social class gradient in both measures of health was significantly wider in the Anglo-Saxon and Southern regimes. We suggest that this evidence supports the notion that welfare regimes continue to explain differences in health according to social class location. We therefore argue that although downward pressures from globalization and neoliberalism have blurred welfare regime typologies, the Nordic model may continue to have an important mediating effect on class-based inequalities in health.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.073
GPT teacher head0.443
Teacher spread0.370 · 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.

Study designQualitative
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

Citations27
Published2017
Admission routes1
Has abstractyes

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