Disability and the Use of Support by Immigrants and Canadian Born Population in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".