Going Local to Global through Technology-Needs Assessment and Development of a Virtual Arctic Youth Wellbeing Network
Bibliographic record
Abstract
Strengths-based approaches to suicide prevention and life promotion in circumpolar regions must engage youth participation and leadership given the impact of suicide on this demographic. We describe the development of a youth-engaged community of practice (CoP) across circumpolar regions, and adaptations to the ECHO model as a foundation for this virtual CoP. We describe youth priorities for learning in the area of mental health and wellbeing, identified through a learning needs assessment. A curriculum was developed to address key areas of interest, including: cultural approaches to mental wellbeing; language-based approaches to mental wellbeing; resilience; government and policy; and suicide prevention. We describe steps taken to adapt the ECHO model, and to introduce Indigenous pedagogical and knowledge sharing approaches into the CoP in order to meet youth learning interests. We conclude that this virtual CoP was a feasible way to create a learning community, and suggest that a priority future direction will be to evaluate the impacts of this virtual CoP on youth engagement, satisfaction and learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".