ANALYZING THE TRENDS OF GOVERNMENT SHARE OF HEALTH BEFORE, DURING, AND AFTER THE COVID-19 PANDEMIC
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".