Resources to support Indigenous reproductive health and justice in Toronto: A respondent-driven sampling study
Bibliographic record
Abstract
In Canada, the reproductive health and rights of Indigenous women, two-spirit, trans, and gender diverse people are threatened by the complex nature of historic and ongoing colonialism. In the face of widespread oppression, however, Indigenous women, two-spirit, trans, and gender diverse people find ways to achieve wellness. To provide novel statistical information about Indigenous reproductive health, this Master’s thesis takes a strengths-based approach to understanding causes of wellness in a cohort of urban Indigenous women, two-spirit, trans, and gender diverse people of reproductive age (n=323). Through a community-based research partnership with the Seventh Generation Midwives of Toronto and the Well Living House, this study uses secondary data collected with respondent-driven sampling (RDS) methods for the community-driven health survey Our Health Counts Toronto. By drawing on community perspectives and Indigenous reproductive justice theories, we hypothesized that four different resources enhance wellness: (1) relationship to land; (2) traditional foods; (3) cultural connectedness; and, (4) Indigenous programs and services. Logistic regression modelling revealed that relationships to land, traditional foods, and Indigenous programs and services were statistically significant to wellness. This study may aid policy makers and service providers in promoting equitable reproductive health care for Indigenous peoples in Toronto and other Canadian cities. Furthermore, this study demonstrates the applicability of critical Indigenous theories and activism to the fields of population health and epidemiology.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".