An inter-country comparison of unofficial payments: results of a health sector social audit in the Baltic States
Bibliographic record
Abstract
BACKGROUND: Cross-country comparisons of unofficial payments in the health sector are sparse. In 2002 we conducted a social audit of the health sector of the three Baltic States. METHODS: Some 10,320 household interviews from a stratified, last-stage-random, sample of 30 clusters per country, together with institutional reviews, produced preliminary results. Separate focus groups of service users, nurses and doctors interpreted these findings. Stakeholder workshops in each country discussed the survey and focus group results. RESULTS: Nearly one half of the respondents did not consider unofficial payments to health workers to be corruption, yet one half (Estonia 43%, Latvia 45%, Lithuania 64%) thought the level of corruption in government health services was high. Very few (Estonia 1%, Latvia 3%, Lithuania 8%) admitted to making unofficial payments in their last contact with the services. Around 14% of household members across the three countries gave gifts in their last contact with government services. CONCLUSION: This social audit allowed comparison of perceptions, attitudes and experience regarding unofficial payments in the health services of the three Baltic States. Estonia showed least corruption. Latvia was in the middle. Lithuania evidenced the most unofficial payments, the greatest mistrust towards the system. These findings can serve as a baseline for interventions, and to compare each country's approach to health service reform in relation to unofficial payments.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".