Exploration des déterminants du soutien social chez les personnes aînées immigrantes et canadiennes de souche
Bibliographic record
Abstract
Aging in Canada is growing fast and the proportion of aged immigrants from diverse origins is growing as well. Therefore, the knowledge related to this portion of the population is very limited because a few studies had been interested to compare aged immigrants and aged non-immigrants in terms of their perception of social support. The aged immigrants as well as the aged non-immigrants are exposed to different social and physical problems. Social support is a key variable to physical and mental health and allows elderly to live longer at home even though they can face functional limitations that can slow them in their everyday tasks. This study proposes to explore, from a secondary analysis of the Canadian Community Health Survey (CCHS) data, the impact of immigrant status on social support and its determinants among elderly. The sample of this study is coming from a geographically restricted sample of the original study that had 130 000 subjects aged over 12 years old and who lived in one of the 122 socio-sanitary sectors of Canada. The sub-sample of the present study counts 848 subjects aged over 60 years old, living in the city of Montreal. The subjects were divided in two groups in terms of their immigration status (207 subjects in the immigrants group, 641 subjects in the non-immigrants group). Perceived social support was measured, using the Medical Outcomes Study-Social Support Survey (MOS-SSS). In order to achieve the objectives of the study, a covariance analysis served in first to evaluate if some, differences occurred in the sub-scales of the social support between the two groups. In a second time, a linear regression analysis has been done to identify the more reliable variable that can predict the score in the different sub-scales for each group. The results of those analyses did not reveal any significant difference related to the perceived social support in the two groups. Also the determinants included in the theoretical model of this study explains a very few variance in the social support. That allows us to think that there are a lot more determinants to identify. Elderly are a heterogenic group, it is very important to diversify to way we get to them and the programs we built if we want to respond correctly to their needs. The present study wants to add on knowledge in a field where it has to be specified.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".