Will the Conversion from Sanatorium Medical Facilities into New Facilities Progress in Japan?
Bibliographic record
Abstract
In Japan, under the Long-Term Care Insurance Act of 2018, the Integrated Facility for Medical and Long-Term Care was established as a new long-term care insurance facility into which Sanatorium Medical Facilities could be converted, and this conversion has taken place gradually; in this study, we compared the management policies between existing Sanatorium Medical Facilities and Integrated Facilities. We also examined the management policies of Geriatric Health Services Facilities. For the management policies of individual facilities, published data on the “Long-Term Care Service Information Publication System” website were used; the study included 142 Integrated Facilities, 245 Sanatorium Medical Facilities, and 237 Geriatric Health Services Facilities. The percentage of facilities in each facility group that included specific keywords was compared. There were no significant differences in the percentage of facilities including “Return,” “Long-term,” “Management,” “Care,” and “Coordination” in their management policies between Sanatorium Medical Facilities and Integrated Facilities. Compared with Geriatric Health Services Facilities, Sanatorium Medical Facilities had a significantly lower rate of including “Return” and a significantly higher rate of including “Long-term,” “Management,” “Care,” and “Coordination.” As seen from the above, the management policies of Sanatorium Medical Facilities were similar to those of Integrated Facilities, rather than Geriatric Health Services Facilities. When Geriatric Health Services Facilities and Integrated Facilities were compared as candidates for conversion from Sanatorium Medical Facilities, it was suggested that the barrier to entry is lower for the Integrated Facilities than for Geriatric Health Services Facilities in terms of necessity of major change in management policies.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".