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Record W2246951456 · doi:10.1177/0020731415611634

Why is There so Much Controversy Regarding the Population Health Impact of the Great Recession? Reflections on Three Case Studies

2015· article· en· W2246951456 on OpenAlexaff
Amaia Bacigalupe, Faraz Vahid Shahidi, Carles Muntaner, Unai Martín, Carme Borrell

Bibliographic record

VenueInternational Journal of Health Services · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsLife expectancyRecessionPopulationPopulation healthPublic healthDevelopment economicsPoliticsExpectancy theoryPolitical scienceMental healthEconomicsSociologyPsychologyMedicineDemographyMacroeconomicsPsychiatry

Abstract

fetched live from OpenAlex

In the aftermath of the Great Recession, public health scholars have grown increasingly interested in studying the health consequences of macroeconomic change. Reflecting existing debates on the nature of this relationship, research on the effects of the recent economic crisis has sparked considerable controversy. On the one hand there is evidence to support the notion that macroeconomic downturns are associated with positive health outcomes. On the other hand, a growing number of studies warn that the current economic crisis can be expected to pose serious problems for the public's health. This article contributes to this debate through a review of recent evidence from three case studies: Iceland, Spain, and Greece. It shows that the economic crisis has negatively impacted some population health indicators (e.g., mental health) in all three countries, but especially in Greece. Available evidence defies deterministic conclusions, including increasingly "conventional" claims about economic downturns improving life expectancy and reducing mortality. While our results echo previous research in finding that the relationship between economic crises and population health is complex, they also indicate that this complexity is not arbitrary. On the contrary, changing social and political contexts provide meaningful, if partial, explanations for the perplexing nature of recent empirical findings.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.161
GPT teacher head0.536
Teacher spread0.376 · 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 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

Citations37
Published2015
Admission routes1
Has abstractyes

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