Collaboratively Adapting Culturally-Respectful, Locally-Relevant Suicide Prevention for Newly Participating Alaska Native Communities
Bibliographic record
Abstract
Because suicide is deeply connected to local, historical and relational contexts, effective suicide prevention strategies must balance maintaining fidelity of evidence-based practices and adapting for the unique needs of diverse communities. Promoting Community Conversations About Research to End Suicide (PC CARES) builds the capacity of local people in close-knit rural Alaska Native communities to take preventative actions based on existing relationships, roles, and priorities. In a series of learning circles, community members learn about multilevel evidence-based suicide prevention practices, apply the information to personal and cultural contexts, and develop plans for taking action—on their own terms—in their lives. Here, we describe the participatory process used to adapt PC CARES from one region of Alaska to another, aiming to maximize transferability, practicality and relevance in our partner communities. With the shared goal of promoting self-determined, evidence-informed, community-based suicide prevention, the adaptation process included negotiating between comprehensiveness and understandability; subject appeal and utility; predictability and customizability, through consensus-building with researchers and community members. Lessons learned can be helpful to others working to navigate community-specific priorities and evidence-based approaches to develop interventions that can work across many different communities.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".