Service Utilization in Long Term Care Insurance and the Effectiveness of the Service
Bibliographic record
Abstract
Due to high speed aging, there is an increasing financial risk in the Long Term Care Insurance in Japan. Hence it is needed that each service provided through this public insurance system has high value for money. As a first step, in evaluating the effectiveness of long term care services, we observed the variations in the severity in need of and care on the service utilizations by individuals' grade in need of and care. We used five-year panel data on LTCI beneficiaries living in Shimane-prefecture in Japan. Samples consist of 68.2% women and 31.8% of men. LTCI beneficiaries are basically above 65 years old. Three quarter of them is above 75 years old. In our observational period (2000-2005), a half of them experienced increase in need of and care. The higher beneficiaries are graded, the smaller fraction of them experienced increase in need: 51.23 % of beneficiaries in support, 34.59% in long-term care 1, 30.97%, 29.10%, 15.86% in requiring long-term care through 2 to In contrast, the mortality rates are higher among beneficiaries with lager need. 26.39% of beneficiaries in support die in five year observational period. The mortality rate is increasing from 40.98%: long-term care 1 to 74.17%: long-term care 4. We evaluated the relationship between the service utilization and the increase in need of and care with regression. We took into account of endogenous property of the variables, whether the beneficiary was dead or alive, whether in-house care or institutional care was chosen, and so on. We discuss about our results with special emphasis on the role of long term care system.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".