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Record W2768600610 · doi:10.15353/cjds.v6i4.382

Population Aging and the Ontario Disability Support Program (ODSP)

2017· article· en· W2768600610 on OpenAlexaffvenueabout
Don Kerr, Tracy Smith‐Carrier, Juyan Wang, Dora M. Y. Tam, Siu Ming Kwok

Bibliographic record

VenueCanadian Journal of Disability Studies · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of CalgaryWestern UniversityThe King's University
Fundersnot available
KeywordsDemographyPopulationPopulation ageingGerontologyDistribution (mathematics)GeographyMedicineSociology

Abstract

fetched live from OpenAlex

The number of beneficiaries on social assistance in Ontario is not of minor importance, with almost a million (964,182) participants province wide in 2016. The number of persons on the Ontario Disability Support Program (ODSP) has rapidly increased, from about 280,000 in 2003 to over 475,000 in late 2016, for a rather dramatic increase of about 70 per cent. The reasons for this increase in ODSP are not straightforward, although population aging has frequently been cited as an important factor contributing to this growth. The primary purpose of the current paper is to provide a quantitative fix as to the relative importance of population growth and shifts in Canada’s age distribution to the rather pronounced increase in ODSP participation. We estimate here that demography alone can be considered responsible for only about 28 per cent of the overall growth in ODSP over the 2003-2014 period. The relatively modest impact of demography was less than initially anticipated in light of the distinct age/sex pattern of ODSP participants and some rather important shifts in the age sex structure of Ontario.

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)0.019
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.395
GPT teacher head0.462
Teacher spread0.067 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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