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Record W761250470

Financing Long-Term Care in Canada

2012· article· en· W761250470 on OpenAlexaboutno aff
Michel Grignon, Nicole F. Bernier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLong-term careBusinessPopulationPublic economicsFinanceActuarial scienceEconomic growthEconomicsMedicineNursingEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

SummaryAs Canada's population ages, a growing number of frail seniors will require long-term care services to help them perform daily activities such as eating, dressing or bathing. Ensuring that adequate care is accessible to every Canadian who needs it should be a national priority.Currently, the financing of long-term care is a patchwork. Access to long-term care and its cost to individuals vary depending on the region where they live and whether they are still at or in a residential facility. How will governments address the anticipated increase in long-term care needs in the next two decades and beyond? Because there is little information about the level of public funding currently available, let alone future funding, Canadians are not in a position to make informed decisions on how to plan for their care needs in the future.This IRPP study examines which financing schemes are most likely to ensure universal coverage of long-term care services in an equitable and efficient way, and what should be the role of governments in that regard. Based on a review of the economics literature and empirical evidence available from other countries, Michel Grignon and Nicole F. Bernier analyze the pros and cons of available options for financing long-term care: private savings, private insurance and universal public insurance.The authors find that relying on private savings is not an efficient way for individuals to provide for their potential future care needs, as they are likely to save too much or too little. While the risk of becoming dependent on formal care for an extended period of time is concentrated among a relatively small segment of the population, for some that risk can reach catastrophic levels in financial terms (for example, needing extensive care for over five years).Long-term care thus warrants some form of insurance, either private or public. Private longterm care insurance, by its nature, is subject to significant market failures. As a result, taking this option would require heavy government regulation and large subsidies. Because of their effect on individuals' decisions and behaviour regarding long-term care insurance, last-resort options, such as obtaining care in public hospitals, would also have to be curtailed. Moreover, individuals would still end up paying more for coverage than they would if they contributed to a public insurance plan.The authors recommend that governments adopt a universal public insurance plan that provides full coverage based on a standard evaluation of care needs. This would reduce uncertainty for Canadians and be more equitable. It would also be more consistent with the aging at home approach, which is favoured by seniors and increasingly promoted by governments.ResumeAvec le vieillissement de la population, de plus en plus de personnes âgees en perte d'autonomie necessiteront des soins de longue duree pour accomplir des tâches quotidiennes telles que manger, se laver et s'habiller. C'est pourquoi l'acces a des soins adequats pour tous les Canadiens qui en ont besoin doit devenir une priorite nationale.Presentement, le financement des soins de longue duree est tres heteroclite. L'acces aux soins et leur cout varient suivant les regions et selon que les soins sont requis a domicile ou en etablissement. Que feront les gouvernements face a la hausse apprehendee des besoins de soins de longue duree au cours des 20 prochaines annees et au-dela ? On trouve si peu d'information sur le financement actuel et futur que les Canadiens ne peuvent prendre des decisions eclairees et se preparer financierement pour le cas ou ils auraient besoin de ces soins.Cette etude de l'IRPP examine les modeles de financement qui pourraient assurer de facon equitable et efficace la couverture universelle des soins de longue duree, de meme que le role des gouvernements en la matiere. S'appuyant sur une revue des etudes economiques et empiriques basees sur l'experience d'autres pays, Michel Grignon et Nicole F. …

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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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

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)0.0010.001

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.

Opus teacher head0.040
GPT teacher head0.426
Teacher spread0.386 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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