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Record W4297514172 · doi:10.3138/cpp.2022-031

The Future of Long-Term Care in Quebec: What Are the Cost Savings from a Realistic Shift toward More Home Care?

2022· article· en· W4297514172 on OpenAlexaffvenueabout
Nicholas‐James Clavet, Réjean Hébert, Pierre‐Carl Michaud, Julien Navaux

Bibliographic record

VenueCanadian Public Policy · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité de MontréalCenter for Interuniversity Research and Analysis on OrganizationsUniversité du Québec à MontréalHEC Montréal
Fundersnot available
KeywordsLong-term careBusinessNeutralityPublic economicsNursing homesTerm (time)Assisted livingResidential careActuarial scienceEconomicsNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

In this article, we aim to estimate future long-term care needs and expenditures in Quebec while proposing and evaluating a reform package that could deliver increased coverage and be more financially sustainable than current policy. This reform package consists of a shift toward more intensive use of home care while increasing public coverage of care needs. A key feature of the proposed reform is to improve users’ ability to choose their provider with the creation of a senior’s care account, an account that allows individuals in need to purchase services from several providers, including both home and institutional care. To improve the neutrality of public support across care arrangements, we also propose an increase in the resident contribution in nursing homes while favouring the continued use of existing tax credits to help seniors with lower care needs. Using detailed dynamic modelling of care needs, living arrangements, and expenditures, we estimate that long-term care needs will grow rapidly in the next two decades, and the costs will quickly become prohibitive under current policy. We show that substantial cost savings may exist.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.331
Teacher spread0.308 · 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.

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

Citations5
Published2022
Admission routes3
Has abstractyes

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