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Record W1500189183 · doi:10.1300/j031v13n02_07

National Consistency and Provincial Diversity in Delivery of Long-Term Care in Canada

2002· article· en· W1500189183 on OpenAlex
Peter C.H. Chan, Susan Kenny

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Aging & Social Policy · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsVancouver Native Health SocietyRick Hansen FoundationRichmond Hospital
Fundersnot available
KeywordsLegislatureService delivery frameworkDiversity (politics)BusinessConsistency (knowledge bases)Quality (philosophy)Service (business)Long-term careHealth carePublic administrationMedicineNursingEconomic growthPolitical scienceMarketingEconomicsComputer science

Abstract

fetched live from OpenAlex

The aim of this article is to demonstrate the diversity in delivery of long-term care at the provincial level, within a national legislative framework that provides universal health insurance and public administration. Not all provinces have legislated provision of long-term care, but mandates for provincial long-term care programs typically address the needs of those with chronic health needs and maintain them in the community for as long as possible. Eligibility is based on common criteria of residency, health need, facility, assessment, and consent. The three common components of the service delivery system are institutional care, community-based services, and home-based services; the kinds of services within each component and the mix among them vary from province to province. There are also five common features in provincial service delivery systems: single point of entry, assessment, client classification, case management, and single administration. Throughout the article, examples from different provinces show the varying ways in which these aspects of service delivery have been addressed, and recent innovations have furthered this diversity. A detailed account of quality management systems also shows that while all provinces have adopted a common set of principles, they use a range of methods to pursue quality of care and to promote good practice.

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.

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)0.000
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 score0.074
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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