The Relief Effect of Copayment Decreasing Policy on Unmet Needs in Targeted Diseases
Bibliographic record
Abstract
Background: Bankrupted households have recently been increased due to excessive medical expenditure in Korea. They have not been protected from economic risk when household's member has severe diseases that need a lot of money for treatment. Purpose of this study examines policy effect by comparing unmet needs' change of policy object households and non-object groups. Methods: We used Korea Health panel 2nd 4th data collected by Korea Institute for Health and Social Affairs and National Health Insurance Service. Analysis subjects were 381 households (pre-policy) and 393 households (post-policy) that had cancer and cardiovascular and cerebrovascular diseases. Since it was major concern that estimates benefit strengthening policy started by certain time, we setup comparing households which had diabetes, hypertension disease. Comparison subjects were 393,247 households, respectively and we evaluated policy effect using difference in difference (DID) model. Results: Although unmet needs of policy object households were higher than non-object groups, policy execution variable affected negative direction. But interaction-term which shows pure effect of policy was not statistically significant. We utilized multi-DID model to examine factors affecting unmet needs causes. Copayment assistance policy did not significantly affect households that responded to 'economic reason,' and 'no have time to visit' for unmet needs causes. Conclusion: The second copayment assistance policy did not significantly give positive effect to beneficiary households than non-beneficiary groups. When we consider that primary purpose of public insurance guarantee high medical expenditure occurred by unexpected events, it needs to deliberate on switch of benefit strengthening policy that can assist vulnerable people. Also, we suggest that government forward a policy covering non-reimbursable medical expenses as well as switch of benefit strengthening direction because benefit policy do not affect non-covered medical cost which accounts for quarter of total health expenditure.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".