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Record W2100590942 · doi:10.1177/0020715213513280

Health care determinants in comparative perspective: The role of partisan politics for health care provision

2013· article· en· W2100590942 on OpenAlexvenueno aff
Ingalill Montanari, Kenneth Nelson

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
FundersVetenskapsrådetWorld Health Organization
KeywordsHealth carePoliticsCompetition (biology)Government (linguistics)ConfessionalPolitical sciencePublic healthCitizenshipHealth policyEconomic growthEconomicsMedicineNursing

Abstract

fetched live from OpenAlex

Health care differs substantially across countries in terms of service provision relevant for social citizenship. Whereas driving forces for the expansion and subsequent decline of cash benefits have received major scholarly interest in comparative research, determinants of developments in public services are less explored. In this article we assess the role of partisan politics for health care provision measured in terms of health employment, hospital beds, and medical technology. Regression analyses based on Organization for Economic Co-operation and Development Health data for 18 countries covering the period 1980–2005 show that partisan politics influences levels of health care provision. Left government strength is positively related to health care provision and the association is driven by developments in health employment. Confessional parties are also associated with high levels of health employment, particularly when they are in close electoral competition with left parties. Both left and confessional government strength is negatively associated with the provision of hospital beds, but these effects are mitigated when parties are in intense electoral competition. In terms of medical technology, we find no partisan political 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 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.074
GPT teacher head0.553
Teacher spread0.478 · 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 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

Citations29
Published2013
Admission routes1
Has abstractyes

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