Health Care as Social Investment? Public Opinion on Trade-Offs Between Curative and Preventive Care in Four OECD Countries
Bibliographic record
Abstract
CONTEXT: The COVID-19 pandemic highlighted the importance of public health programs in preventing diseases and providing health security for entire populations. Yet, governments invest very little in preventive health care. While it is generally assumed that this lack of public investment reflects individuals' lack of interest in public health, few studies have actually examined the public's preferences on this issue. Drawing on the literature on social investments, this article brings politics into the study of individuals' preferences for public health and curative care. METHODS: The authors rely on an original survey conducted in four OECD countries among 8,000 respondents to assess how citizens trade off preventive and curative care. FINDINGS: The authors show that higher trust and liberal social values are associated with support for preventive health care, as both variables correlate with support for policies whose benefits unfold in the long term. By contrast, individuals with poor self-rated health and low satisfaction with health care services prioritize expenditures in curative care that are beneficial to them in the short term. CONCLUSIONS: These findings advance previous research by identifying the groups that demand additional investments in public health and those who prefer to allocate more resources toward curative care.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".