Highlights from Help Wanted? Providing and Paying for Long-
Bibliographic record
Abstract
� Despite a comprehensive system, Japan has succeeded in containing long-term care (LTC) expenditure to levels below that of other comprehensive LTC systems such as those in Nordic countries. However Japan public spending on long-term care is projected to more than double from 1.4 % in 2007, and could even reach 4.4 % of GDP in 2050. Japan’s real public LTC spending is expected to grow at a faster rate between 2006 and 2025. � Japan has implemented several policies to attract and retain care workers. In 2009, a fund was set up to assist providers in offering higher salaries. There are various subsidies available to attract young people in the LTC sector and for training for job leavers or for those who are currently working in other sectors. � By 2050, the demand for LTC workers is expected to about double in Japan, as in the United-States and Canada. However, the total workforce in the economy is set to decline in Japan, and the need for care will grow in line with the number of people aged over 80 years in the population. In light of the limited inflow of care workers from other countries, the ability to recruit enough domestic workers to the sector will be of utmost importance in the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".