SP6-2 Characteristics and determinants of self-rated health of minority Francophone seniors living in Canada and their access to health services in French
Bibliographic record
Abstract
Research in the US particularly has contributed to a lot of what is known about the difficulties that linguistic minorities face in accessing and using health services. Studies need to be conducted in Canada to refine and improve knowledge on the matter. The literature is virtually unanimous that seniors do not enjoy the same level of health as the general population. Canadian studies have found that the health status of seniors declines with increasing age as more health issues are reported. This has also been linked to health services use by seniors aged 65 and over accounting for over 47% of total healthcare cost increase. The 2006 Canadian post-census Survey on the Vitality of Official-Language Minorities (SVOLM) carried out by Statistics Canada is used as well as the 2007 Canadian Community Health Survey (CCHS). The SVOLM helps assess factors associated with the self-rated health of minority Francophone seniors. The CCHS helps complement the SVOLM and allows for comparability with the general population. Descriptive, univariate and multivariable analyses such as ordinary and binary logistic regression are carried out. Through a social marketing approach, the results of the quantitative analyses (which are currently being carried out) will help engage dialogue with the community, educators, policy makers, health practitioners, and the healthcare system in order to help inform and shape policy with regards to health services access and utilisation in the province of Saskatchewan, particularly in the Saskatoon Health Region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".