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Record W4306382019 · doi:10.3390/ijerph192013290

Going Local to Global through Technology-Needs Assessment and Development of a Virtual Arctic Youth Wellbeing Network

2022· article· en· W4306382019 on OpenAlexafffund
Allison Crawford, Brittany Graham, Arnârak Patricia Bloch, Alexis Bornyk, Selma Ford, David Mastey, Melody Teddy, Christina Viskum Lytken Larsen

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMcMaster UniversityUniversity of TorontoInuit Circumpolar CouncilCentre for Addiction and Mental Health
FundersMitacsGovernment of Canada
KeywordsMental healthIndigenousPositive Youth DevelopmentPsychologyPromotion (chess)Government (linguistics)CurriculumPublic relationsApplied psychologyPedagogyPolitical scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.479
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes2
Has abstractyes

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