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Record W2903313707 · doi:10.15353/cjds.v7i3.450

Disability and the Use of Support by Immigrants and Canadian Born Population in Canada

2018· article· en· W2903313707 on OpenAlexaffvenueabout
Stine Thestrup Hansen, K. Bruce Newbold, Robert Wilton

Bibliographic record

VenueCanadian Journal of Disability Studies · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster University
Fundersnot available
KeywordsImmigrationDescriptive statisticsPopulationMultivariate analysisDemographyGerontologyDemographic economicsMedicineGeographySociology

Abstract

fetched live from OpenAlex

Immigrants account for a large proportion of Canada’s population. Despite an emphasis on immigrant health issues within the literature, there is surprisingly limited attention given to disability within the immigrant population, although differential prevalence rates between immigrants and the Canadian born population have been noted. The observed differences in prevalence rates by gender and immigrant status raise questions around the use of support services. In this paper, analysis draws on Statistics Canada’s 2006 Participation and Activity Limitation Survey (PALS). A mix of descriptive and multivariate techniques are used to explore who provides support, differences in the use of support between immigrants and the Canadian born and need for additional support. The descriptive results suggest that there was a broad parity in terms of the use of support, with immigrants and Canadian born nearly equally likely to use support. Use of support was also greater amongst those with a more severe disability. Multivariate analysis revealed that particular sub-groups of immigrants, and in particular immigrant females, severely disabled immigrants, and some age, income and educational groups were less likely to use support after controlling for other correlates of use. The difficulties confronted by people with disabilities appear to be magnified within the immigrant community, and particularly amongst sub-groups of the immigrant population.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.049
GPT teacher head0.315
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2018
Admission routes3
Has abstractyes

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