Understanding the intergenerational effects of colonization: Aboriginal women with neurological conditions—their reality and resilience
Bibliographic record
Abstract
The “Understanding from Within” (UFW) project was part of the National Health Population Study of Neurological Conditions (NHPSNC), a 4-year study aimed at better understanding the scope of neurological conditions in Canada, and funded by the Public Health Agency of Canada. The goal of the UFW project was to develop a better understanding of how Aboriginal people conceptualize neurological conditions and the impacts on their families and communities, and the resources and supports needed to provide culturally safe and appropriate care. The research was qualitative and used an Indigenous Research Methodologies (IRM) approach to guide the design, collection of data, and analysis. Two methods were used to collect information: in-depth interviews and research circles (focus groups). A total of 80 people participated in the research, 69 women and 11 men. In-depth interviews were undertaken with key informants (22), with Aboriginal people living with a neurological condition (18), and with Aboriginal people caring for someone with a neurological condition (40). This paper examines the physical, mental, emotional, and spiritual impacts of neurological conditions on Aboriginal people, primarily women. It also examines other themes that emerged from the narratives, including recommendations to healthcare providers and cross-cutting themes that are relevant to culturally safe care and how it relates to neurological conditions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".