Design and implementation of the Our Health Counts (OHC) methodology for First Nations, Inuit, and Metis (FNIM) health assessment and response in urban and related homelands
Bibliographic record
Abstract
OBJECTIVES: Methods for enumeration and population-based health assessment for First Nations, Inuit, and Metis (FNIM) living in Canadian cities are underdeveloped, with resultant gaps in essential demographic, health, and health service access information. Our Health Counts (OHC) was designed to engage FNIM peoples in urban centres in "by community, for community" population health assessment and response. METHODS: The OHC methodology was designed to advance Indigenous self-determination and FNIM data sovereignty in urban contexts through deliberate application of Indigenous principles and linked implementation strategies. Three interwoven principles (good relationships are foundational; research as gift exchange; and research as a vehicle for Indigenous community resurgence) provide the framework for linked implementation strategies which include actively building and maintaining relationships; meaningful Indigenous community guidance, leadership, and participation in all aspects of the project; transparent and equitable sharing of project resources and benefits; and technical innovations, including respondent-driven sampling, customized comprehensive health assessment surveys, and linkage to ICES data holdings to generate measures of health service use. RESULTS: OHC has succeeded across six urban areas in Ontario to advance Indigenous data sovereignty and health assessment capacity; recruit and engage large population-representative cohorts of FNIM living in urban and related homelands; customize comprehensive health surveys and data linkages; generate previously unavailable population-based FNIM demographic, health, and social information; and translate results into enhanced policy, programming, and practice. CONCLUSION: The OHC methodology has been demonstrated as effective, culturally relevant, and scalable across diverse Ontario cities.
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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 | 0.079 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".