COVID-19 & HEALTHCARE (RE)PRIVATIZATION? A COMPARATIVE ANALYSIS OF HIGH AND MEDIUM PER CAPITA GDP COUNTRIES
Bibliographic record
Abstract
This paper examines whether the COVID-19 pandemic accelerated healthcare privatization in high- and medium-GDP countries. Drawing on political economy theories of path dependence and privatization, we analyze changes in public and private healthcare expenditures in eight countries: Canada, Chile, Denmark, Georgia, Luxembourg, Norway, the Philippines, and South Korea—comparing data from 2019 and 2022. Using World Bank indicators on healthcare financing, GDP, aging populations, and food insecurity, we find that although healthcare expenditures generally increased during the pandemic, this increase was primarily in public, not private, spending. Case studies of Georgia and South Korea reveal that local economic and demographic pressures, such as food insecurity and population aging, influenced healthcare financing patterns, but did not result in structural shifts toward privatization. Our findings suggest that while the pandemic placed extraordinary strain on healthcare systems, it did not trigger a widespread move toward private healthcare in the countries studied. Instead, we observe limited evidence of de-privatization in some higher-income cases. These findings challenge claims that crises necessarily accelerate market-based healthcare reforms and point to the need for further research with larger samples and longer-term data to evaluate post-pandemic policy trajectories globally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".