Linking Databases in Collaborative and Culturally Safe Ways to Evaluate the Effectiveness of PAX-Good Behaviour Game (PAX) in First Nations Communities.
Bibliographic record
Abstract
ObjectivesThe overarching research objective was to examine, culturally adapt, and further evaluate a mental health promotion approach called the PAX within 8 First Nations communities. This presentation describes a research process whereby First Nations community members and researchers worked in collaborative and culturally safe ways to reach their research objectives. ApproachBuilding on a strong existing relationship between Swampy Cree Tribal Council (SCTC) members from Northern Canada and academic researchers, a team was formed to prepare the research proposal. This team included community members, leaders from First Nations organizations, decision makers, program developers and researchers. This research was guided by two-eyed seeing, a principle developed by a Mi’kmaw Elder, that recognizes both Indigenous and Western ways of knowing, where one worldview does not dominate the other. The research process was compliant with Ownership, Control, Access, and Possession (OCAP) principles that ensure self-determination of First Nations communities over research involving their people. ResultsA First Nations community liaison was hired as a research team member ensuring that traditional and cultural protocols were adhered to and connections to community members facilitated and sustained. Over the course of the research, the team met monthly to oversee implementation and annually with SCTC community members for guidance and for sharing and interpreting results. All 8 communities were actively engaged and benefitted from their involvement. Seeing the value of examining PAX’s effectiveness through linkages to administrative datasets, community members supported engagement of an additional 16 First Nations communities thereby ensuring an adequate sample size for the study. Health and education databases were linked to program data from 20 First Nations communities. Infographics, lay summaries and presentations were prepared for meaningful knowledge exchange. ConclusionFirst Nations communities deemed it essential to understand what works and for whom regarding mental health promotion. Building relationships with First Nations community members based on trust and respect provided information that was relevant and beneficial to their communities. This relationship-building should be considered when developing research timelines and budgets.
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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 | 0.053 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".