Assisted-living for older people in Israel: market control or government regulation?
Bibliographic record
Abstract
In recent decades there has been a rapid expansion of assisted-living facilities for older people in many different countries. Much of this growth has occurred with only limited or no government regulation, but many problems have arisen, typically around the quality of care, which have led to demands that governments act to protect vulnerable residents. This paper examines whether formal legal regulation is the optimal policy to protect the needs and rights of frail residents, while respecting the legitimate interests of others, such as operators and owners. It presents the case for and against direct legal regulation (as in institutions), and suggests that no overall a priori assessment is possible. The analysis is based on the case of Israel, where proposed regulations for assisted-living have been introduced but not implemented. After a brief history of assisted-living in Israel – its recent dramatic growth and why this occurred – the paper concludes that formal direct regulation is not the best route to follow, but that the better course would be to develop totally new ‘combined’ regulatory legislation. This would define the rights of residents and encourage self-regulation alongside minimal and measured mechanisms of deterrence. Such an approach could promote the continued development of the assisted-living industry in Israel and elsewhere, while guaranteeing that the rights, needs and dignity of older residents are protected.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".