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How Society Shapes the Health Gradient: Work-Related Health Inequalities in a Comparative Perspective

2012· review· en· W2142967451 on OpenAlexaff
Chris McLeod, Peter A. Hall, Arjumand Siddiqi, Clyde Hertzman

Bibliographic record

VenueAnnual Review of Public Health · 2012
Typereview
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of TorontoInstitute of Population and Public HealthLearning PartnershipInstitute of Health Services and Policy ResearchPublic Health OntarioUniversity of British Columbia
Fundersnot available
KeywordsUnemploymentInequalityReceiptCapitalismWork (physics)Perspective (graphical)Demographic economicsEconomicsSociologyEconomic growthPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Analyses in comparative political economy have the potential to contribute to understanding health inequalities within and between societies. This article uses a varieties of capitalism approach that groups high-income countries into coordinated market economies (CME) and liberal market economies (LME) with different labor market institutions and degrees of employment and unemployment protection that may give rise to or mediate work-related health inequalities. We illustrate this approach by presenting two longitudinal comparative studies of unemployment and health in Germany and the United States, an archetypical CME and LME. We find large differences in the relationship between unemployment and health across labor-market and institutional contexts, and these also vary by educational status. Unemployed Americans, especially of low education or not in receipt of unemployment benefits, have the poorest health outcomes. We argue for the development of a broader comparative research agenda on work-related health inequalities that incorporates life course perspectives.

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)0.026
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0000.003
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.004
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 head0.372
GPT teacher head0.526
Teacher spread0.154 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations74
Published2012
Admission routes1
Has abstractyes

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