Concept Mapping: Application of a Community-Based Methodology in Three Urban Aboriginal Populations
Bibliographic record
Abstract
The goal of this research was to develop accessible and culturally relevant urban Aboriginal health information in Ontario. Concept mapping was used to engage Aboriginal stakeholders in identifying health concerns and priorities, with key stakeholders participating from three communities: First Nations people in Hamilton through De dwa da dehs ney>s Aboriginal Health Access Centre (DAHC), Inuit people in Ottawa through Tungasuvvingat Inuit Family Health Team (TIFHT) and Métis people in Ottawa through the Métis Nation of Ontario (MNO). Each community participated in the three concept-mapping activities and generated statements regarding health and health related issues in their communities. Concept systems software was used to create initial cluster maps, which were finalized during map interpretation sessions. Each of the clusters on the unique community maps represented a community health domain. The chosen domain labels and their ratings strongly reflected local First Nations, Inuit, and Métis understandings of health. Concept mapping is found to be an effective and culturally relevant community-based method for urban Aboriginal health research, building on traditional indigenous methods, encouraging cross-community participation and contributing to three unique health assessment tools that challenge existing illness-based narratives for these populations and reflect indigenous-specific social determinants of health.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".