Listening to First Nations women’ expressions of heart health: mite achimowin digital storytelling study
Bibliographic record
Abstract
Historically, heart health was approached holistically by First Nations (FN) peoples, which was integrated into daily living. Caring for the physical, emotional and spiritual needs of individuals, community, family, and the living environment was integral. The Truth and Reconciliation Commission of Canada demonstrates the decimation of health practices through governmental policy to destroy the cultural foundations of FN peoples. Relational systems and ways of living were outlawed, and the health of FN people suffered. A digital storytelling study collaborated with Manitoba FN women with lived experience of caring for a biomedical-diagnosed heart condition. The objective was to identify concepts, language, and experiences of heart health among FN women. Six women created five digital stories; four are available publically online. Themes addressed by the storytellers include: changes to diet and lifestyle, related health conditions, experiences with healthcare system, residential schools, and relationships with children and grandchildren. The intersection of Western and FN knowledges heard in the women's stories suggests heart health knowledge and care is embedded within historical and social contexts. Insights into the non-dichotomous relationship between FN and biomedical knowledge of heart health, along with their conceptualisations of heart, suggests historical and social roots underlying heart health issues First Nations women face.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".