Population Aging and Healthcare Expenditure in Korea
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Korea's rapid population aging has been considered as a major factor in increase of healthcare expenditure (HCE). However, there were no clear empirical evidences in Korea that show if population aging has a significant impact on HCE. To examine the 'red herring' argument, this study used Heckman, two-part, and augmented model with Korean National Health Insurance claim data for the deceased and survivors of aged 20 years and over verified by Korean National Health Insurance Service between January 1 and December 31, 2010. Our results suggest that when time to death is controlled for as explanatory variable, HCE decreases as a function of age, and HCE during the terminal year increases as a function of time to death, and HCE in the last quarter of life decreases with age. Therefore, this study affirms that there is no age effect in Korea experiencing the most rapid population aging among Asian countries. An increase in the number of elderly, due to the aging of baby boomers, may not increase a share of HCE out of gross domestic product (GDP) in Korea. Copyright © 2015 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it