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Record W1521758672 · doi:10.1111/ssqu.12076

The Economic Crisis and Medical Care Use: Comparative Evidence from Five High‐Income Countries

2014· article· en· W1521758672 on OpenAlexaboutno aff
Annamaria Lusardi, Daniel Schneider, Peter Tufano

Bibliographic record

VenueSocial Science Quarterly · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersNetwork for Studies on Pensions, Aging and RetirementPrinceton UniversityRobert Wood Johnson Foundation
KeywordsFinancial crisisContext (archaeology)Medical careFragilityDemographic economicsFinancial fragilityHealth careMeasures of national income and outputEconomicsEconomic growthDevelopment economicsBusinessMedicineGeographyMacroeconomicsFamily medicine

Abstract

fetched live from OpenAlex

Objective We examine how the economic crisis has affected individuals’ use of routine medical care and assess the extent to which the impact varies depending on national context. Methods Data from a new cross‐national survey fielded in the United States, Great Britain, Canada, France, and Germany are used to estimate the effects of employment and wealth shocks and financial fragility on the use of routine care. Results We document reductions in individuals’ use of routine nonemergency medical care in the midst of the economic crisis. Americans reduced care more than individuals in Great Britain, Canada, France, and Germany. At the national level, reductions in care are related to the degree to which individuals must pay for it, and within countries, reductions are linked to shocks to wealth and employment and to financial fragility. Conclusions The economic crisis has led to reductions in the use of routine medical care, and systems of national insurance provide some protection against these effects.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.415
Teacher spread0.373 · 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 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

Citations43
Published2014
Admission routes1
Has abstractyes

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