The Impact of Socio-economic Status on Métis health: A Brief Introduction for Community
Bibliographic record
Abstract
The lack of research on the relationship between socio-economic status and health has hampered efforts to lobby, and seek funding from, municipal, provincial, and federal governments for programs that address socio-economic and health disparities.This paper is a synopsis of research which explores these issues by analyzing the Aboriginal Peoples Survey, the 2001 Census, and the 2001 Canadian Community Health Survey.Results indicate that Métis suffer a lower socioeconomic status (lower incomes, wages, employment, and levels of education attainment).This is in spite of similar levels of involvement of Métis and non-Aboriginal Canadians in the workforce.Even among individuals with similar education, Métis earn lower incomes compared to non-Aboriginal Canadians.Clearly, class and race issues combine to affect the socioeconomic status of Métis.The lower socio-economic status among Métis appears to affect their health.Specifically, Métis with low income and education report lower self-rated health compared to non-aboriginal Canadians with low income and education.While these issues need to be further examined and better quality data are required, preliminary results emphasize the need for programs to address socio-economic disparities, and race and class issues, in order to attain optimal health and well-being for the Métis in Canada.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".