Integrating Ethnicity and Migration As Determinants of Canadian Women's Health
Bibliographic record
Abstract
HEALTH ISSUE: This chapter investigates (1) the association between ethnicity and migration, as measured by length of residence in Canada, and two specific self-reported outcomes: (a) self-perceived health and (b) self-reports of chronic conditions; and (2) the extent to which these selected determinants provide an adequate portrait of the differential outcomes on Canadian women's self-perceived health and self-reports of chronic conditions. The 2000 Canadian Community Health Survey was used to assess these associations while controlling for selected determinants such as age, sex, family structure, highest level of education attained and household income. KEY FINDINGS: * Recent immigrant women (2 years or less in Canada) are more likely to report poor health than Canadian-born women (OR = 0.48 CI: 0.30-0.77). Immigrant women who have been in Canada 10 years and over are more likely to report poor health than Canadian-born women (OR = 1.31 CI: 1.18-1.45).* Although immigrant women are less likely to report chronic conditions than Canadian-born women, this health advantage decreased over time in Canada (OR from 0.35 to 0.87 for 0-2 years to 10 years and above compared with Canadian born women). DATA GAPS AND RECOMMENDATIONS: * Migration experience needs to be conceptualized according to the results of past studies and included as a social determinant of health above and beyond ethnicity and culture. It is expected that the upcoming longitudinal survey of immigrants will help enhance surveillance capacity in this area.* Variables need to be constructed to allow women and men to best identify themselves appropriately according to ethnic identity and number of years in the host country; some of the proposed categories used as a cultural group may simply refer to skin colour without capturing associated elements of culture, ethnicity and life experiences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".