Strength-based Indigenous public health emergencies research: Tl’etinqox methodology
Bibliographic record
Abstract
By using Indigenous research methodologies with a strengths-based approach, the Tl’etinqox (Anaham) research team highlights sustainable community-led solutions suitable for the current and future public health emergencies. The purpose of the study was to identify Indigenous community-led solutions to public health emergencies, particularly climate change and pandemics in addition to build Tl’etinqox community research capacity through intergenerational cultural knowledge exchange. The research methods undertaken in this work describes study procedures developed by an Indigenous strength-based public health research community group that was comprised of an intergenerational Tl’etinqox study team from youth to Elder members working together. The strength-based Tl’etinqox research methods involved leadership engagement, online and offline family cluster recruitment, Healing Circle data collection, oral and written Nenqayni chi’h (Indigenous language) translations, and land-based knowledge mobilization and translation. A key finding is collective efforts led to a localization situated in Tl’etinqox knowledges of Indigenous health research methods, including an independent intergenerational community-based Indigenous research team. Tl’etinqox research methodology yielded a local, culturally-grounded approach that represents a strength-based Indigenized health research methods. Tl’etinqox research team members who led the study incorporated Tŝilhqot’in (Chilcotin) cultural practices, teachings, language, and land as essential components of the research methods. The relevance of the present study describes a localization approach of strength-based Indigenous community-based research, which has meaningful implications for understanding public health emergencies arising from the COVID-19 pandemic and climate change.
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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 | 0.102 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".