Why Did the Policy to Convert Hospitals Into Facilities Not Work in Japan?
Bibliographic record
Abstract
The government of Japan formulated measures to significantly reduce the number of hospital beds for long-term care in 2006. In particular, long-term care hospital beds covered by long-term care insurance (sanatorium medical facilities) were to be abolished in 2012, and existing sanatorium medical facilities were to be converted into long-term care insurance services such as geriatric health services facilities. However, the conversion did not progress in spite of various support measures, and the deadline for abolishment was extended. In order to clarify the reason for this, we selected 28 hospitals with 402 or more long-term care beds and 28 health services facilities with 158 or more beds and examined their management philosophies and analyzed the keywords included. The most popular keyword was “community” in both hospitals and facilities. Hospitals had a significantly higher rate of 60.7% (P< 0.05) of including “trust” or “feeling of relief” in their management philosophies. Facilities had higher rates of including any of the terms “return” or “independence” or “home” (32.1%, P= 0.051), and also of including either “service” or “care” (46.1%, P< 0.05). In conclusion, it is suggested that hospitals with long-term care beds differentiate themselves from neighboring facilities in that they are able to simply accept the situation and be responsible for terminal care whenever inpatients may have difficulty returning home. In addition, it seemed difficult for hospitals to convert into health service facilities, the aim of which is to enable residents to return home.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".