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Record W2575775661

Can Canada Pension Plan Disability Benefit program have better return-to-work incentives for full labor market integration of beneficiaries, reducing the cost of the program?

2013· article· en· W2575775661 on OpenAlexaboutno aff
Shella Mithani

Bibliographic record

VenueuO Research (University of Ottawa) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveWork (physics)BusinessPlan (archaeology)Incentive programPensionPension planLabour economicsActuarial scienceFinanceEconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a brief summary on Canada Pension Plan Disability Benefit (CPPD), the single largest public long-term disability insurance program in Canada. The CPPD provides income security to those people who have suffered prolonged and severe disability that makes a person incapable of pursing any substantially gainful occupation. The paper discusses the issues that CPPD faces today which includes the number of growing beneficiaries and the increasing cost of disability benefits. Each year between 1997-2007 payments rose to 2.2% per year and the number of recipients grew by 1.4% per year (HRSDC 2011a). One of the major issues faced by CPPD is the welfare dependency on disability benefits where people with health conditions leave labor market permanently and never return to work despite their willingness and ability to work. This creates labor shortage for the Canadian economy and consequently increases the cost of disability benefits. The purpose of this paper is to look into this problem specifically and analyze the following research question “Can Canada Pension Plan Disability Benefit program have better return-to-work incentives for full labor market integration of beneficiaries, reducing the cost of the program?” The paper focuses on return to work incentives offered by CPPD and makes a comparative analysis of what other OECD countries are doing to integrate people with health conditions into the labor market in order to reduce the cost of their disability benefits. The paper highlights the findings for all OECD countries where deep economic downturns tend to hit disabled people more than the general working age population and hence increases the beneficiary caseload. There are number of reforms introduced by OECD countries to effectively integrate people with health conditions into the labor market. These reforms include countries being more focused on remaining work capacity, making job search activity a requirement for people on disability benefits, offering partial benefits instead of permanent benefits while encouraging people with health conditions to continue applying for employment, moving towards single age benefits, engaging employers and medical professionals, evaluating labor market programs, giving right services at the right time. All these reforms have had a positive impact in reducing the number of beneficiaries and thus reducing the cost of disability benefits, integrating disabled people into the labor force in an effective manner. Canada, like many other OECD countries, shares similar problems including low rates of employment and high poverty risk for people with disability. The report concludes by presenting policy recommendations for Canada in order to improve the effectiveness of its policies for individuals suffering from disability. Some of the policy recommendations include better coordination between federal and provincial governments, evaluation and monitoring of the labor market programs, one stop-shop model, easy access to employment support programs, providing right services at the right time and enhancing employers’ role in managing sickness absences for workers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.137
GPT teacher head0.377
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2013
Admission routes1
Has abstractyes

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