Assessing barriers to health insurance and threats to equity in comparative perspective: The Health Insurance Access Database
Bibliographic record
Abstract
BACKGROUND: Typologies traditionally used for international comparisons of health systems often conflate many system characteristics. To capture policy changes over time and by service in health systems regulation of public and private insurance, we propose a database containing explicit, standardized indicators of policy instruments. METHODS: The Health Insurance Access Database (HIAD) will collect policy information for ten OECD countries, over a range of eight health services, from 1990-2010. Policy indicators were selected through a comprehensive literature review which identified policy instruments most likely to constitute barriers to health insurance, thus potentially posing a threat to equity. As data collection is still underway, we present here the theoretical bases and methodology adopted, with a focus on the rationale underpinning the study instruments. RESULTS: These harmonized data will allow the capture of policy changes in health systems regulation of public and private insurance over time and by service. The standardization process will permit international comparisons of systems' performance with regards to health insurance access and equity. CONCLUSION: This research will inform and feed the current debate on the future of health care in developed countries and on the role of the private sector in these changes.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".