Nuts'a'maat shqwaluwun — Knitting ways of life with Indigenous research principles to examine preterm birth in Quw'utsun
Bibliographic record
Abstract
SETTING: The Quw'utsun Preterm Birth Study used a community-led and participatory action research methodology to investigate preterm birth in Quw'utsun, a First Nations community in Cowichan Valley, British Columbia (BC). Quw'utsun people and staff from the community's Ts'ewulhtun Health Centre partnered with the BC First Nations Health Authority, Island Health (regional health authority), and the University of British Columbia to develop Nuts'a'maat shqwaluwun (one heart, one mind), a framework for conducting research activities. INTERVENTION: Guided by Elders, Nuts'a'maat shqwaluwun incorporated Quw'utsun standards for research ethics by knitting together snuw'uy'ulh (ways of life), such as Stsi'elh stuhw tu Sul-hween (honour the Elders), with federal policy for ethical conduct of research involving Indigenous people. Situating the study at Cowichan Tribes strengthened the community's authority to lead. OUTCOME: The framework, Nuts'a'maat shqwaluwun, fostered a research environment where we could Ti'tul'atul' tst (learn from one another). We learned to bring our knowledges together to conduct the study in ways that respected snuw'uy'ulh. This research was meaningful to Quw'utsun people because snuw'uy'ulh were respected. Our partnerships resulted in the first-ever report of preterm birth rates and risk factors among Quw'utsun people. Knowledge translation activities enhanced community access to results. IMPLICATIONS: Indigenous Peoples have an inherent and legislated right to self-determination, including the right to lead research involving them. Several principles within Nuts'a'maat shqwaluwun enabled Quw'utsun people to lead this research: (1) trusting relationships; (2) respecting community-specific ways of life; (3) community ownership and access to data; and (4) training opportunities to lead research.
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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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".