No Country for Sick Men: The Political Determinants of Health Policy in Poland
Bibliographic record
Abstract
CONTEXT: The objective of this article is to explain the political factors determining the relatively weak performance of the Polish health care (HC) sector. This can be treated as a critical case for several reasons. First, the Poles are among the most unsatisfied patients in the European Union, with one of the lowest life expectancy levels. Second, Poland spends one of the lowest shares of gross domestic product on HC-related expenditures among OECD countries. Third, the country is facing medical personnel shortages. METHODS: The analysis is based on the mixed-methods approach. The authors rely on quantitative data outsourced from a survey, which is supplemented by the semistructured, in-depth interviews with selected key HC stakeholders representing patients' advocacy groups, medical personnel organizations, and high-level decision-makers. FINDINGS: The Polish HC system remains weak due to the postcommunist legacy in terms of organization, a short-term approach by politicians, and weak decision-making processes. CONCLUSIONS: The HC policy inertia in Poland is determined by a group of interrelated political factors that effectively block the development of any positive 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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".