The Culture is Prevention Project: Adapting the Cultural Connectedness Scale for Multi-Tribal Communities
Bibliographic record
Abstract
The Culture is Prevention Project is a multi-phased communitybased participatory research project that was initiated by six urban American Indian and Alaska Native (AI/AN) health organizations in northern California. Issues driving the project were: i) concerns about the lack of culturally informed or Indigenous methods of evaluating the positive health outcomes of culture-based programs to improve mental health and well-being; and ii) providing an approach that demonstrates the relationship between AI/AN culture and health. Most federal and state funding sources require interventions and subsequent measures focused on risk, harm, disease, and illness reduction, rather than on strength, health, healing, and wellness improvement. This creates significant challenges for AI/AN communities to measure the true impact of local strength and resiliency-based wellness programs. This paper focuses on the methods and results from Phase 3 of the Culture is Prevention Project where we adapted the 29-item Cultual Connectedness Scale (CCS), developed in Canada, to be appropriate for California's multi-tribal communities. The resulting new Cultural Connectivity Scale - California (CCS-CA) was developed by urban AI/AN people for urban AI/AN people. The process, instrument, how to adapt for your community, and implications are reviewed.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".