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Record W7110631953

ANALYZING THE TRENDS OF GOVERNMENT SHARE OF HEALTH BEFORE, DURING, AND AFTER THE COVID-19 PANDEMIC

2025· article· W7110631953 on OpenAlexaboutno aff

Bibliographic record

VenueThe Open Repository - Binghamton (Binghamton University) · 2025
Typearticle
Language
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Public healthTypologyAccountabilityPandemicInvestment (military)Health careGovernment spending
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates whether the COVID-19 pandemic functioned as a critical juncture that led to lasting institutional change in government health investment. Using an original comparative framework, the analysis explores changes in health financing across ten OECD countries classified by Reibling et al.'s five-type typology of healthcare systems. We assess shifts in public, private, and government health expenditure before, during, and after the pandemic, using both descriptive and visual data. Our findings suggest that while nearly all countries increased government and public shares of health spending during the pandemic, the persistence of these changes varied by system type, GDP level, and institutional configurations of authority, responsibility, and accountability. High-income countries with strong accountability mechanisms, such as Canada and Denmark, maintained increased health investment post-crisis. In contrast, countries like Hungary and Slovenia reverted to pre-pandemic patterns, often due to weak public demand or financial constraints. Type 3 and 4 systems demonstrated greater resistance to structural change, while type 5 systems, despite private-sector dominance, saw notable increases in public investment. Our results underscore how institutional design and fiscal capacity jointly determine whether crisis-induced shifts in policy become entrenched or merely temporary, providing insight into the conditions for durable health system reform.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0070.002
Scholarly communication0.0000.000
Open science0.0040.005
Research integrity0.0010.002
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 head0.049
GPT teacher head0.361
Teacher spread0.312 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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