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Record W2103620910 · doi:10.3138/cpp.2014-079

Welfare States and Health Inequalities

2015· article· en· W2103620910 on OpenAlexvenueno aff
Olle Lundberg, Monica Åberg Yngwe, Kersti Bergqvist, Ola Sjöberg

Bibliographic record

VenueCanadian Public Policy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityWelfareWelfare stateUnemploymentPublic economicsSet (abstract data type)Field (mathematics)Health policyEconomicsPolitical scienceEconomic growthHealth careMathematicsComputer sciencePolitics

Abstract

fetched live from OpenAlex

While much research points to the importance of a range of welfare state policies to reduce inequalities in health, the growing literature in this field is full of mixed and contradictory results. In this paper, we provide a brief discussion about the different conceptual and methodological approaches used in comparative research on the relationship between welfare policies and health. Against a theoretical discussion of possible linkages among one central welfare policy, unemployment benefit schemes, and health, we also provide examples of findings on how two central dimensions of such schemes—coverage and replacement rates—are linked to health and health inequalities across Europe. These examples indicate not only that welfare state programs can contribute to smaller health inequalities but also that their effectiveness in this respect depends on their institutional set-up.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.998

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 head0.185
GPT teacher head0.440
Teacher spread0.254 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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