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Record W2029949961 · doi:10.1017/s0144686x03001417

Assisted-living for older people in Israel: market control or government regulation?

2003· article· en· W2029949961 on OpenAlexaff
Israel Doron, Ernie Lightman

Bibliographic record

VenueAgeing and Society · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDignityLegislationGovernment (linguistics)Deterrence theoryControl (management)BusinessPolitical sciencePublic administrationPublic economicsEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.333
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2003
Admission routes1
Has abstractyes

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