Exploring Thriving of Inuit Youth Through an Engagement Lens: A Strengths-Based Focus on Factors Related to Nunavummiut Youth's Participation in a Psycho-Educational Mental Health Project
Bibliographic record
Abstract
Few studies have investigated strategies to support Inuit youth engagement in mental wellness using a strengths-based, culturally grounded approach. Existing literature primarily focuses on environmental science and addresses solely physical and educational barriers. The current study aimed to identify multi-systemic factors that may directly or indirectly support Inuit youth leaders and participants engagement in two mental wellness research initiatives: the Making I-SPARX Fly in Nunavut [I-SPARX] and the Virtual Qaggiq projects. For Inuit youth leaders/ research assistants, this was explored through semi-structured interviews. For Inuit youth testers in the I-SPARX game evaluation trial, demographics and response patterns on a pre/post intervention wellness questionnaires were analyzed. Thematic analysis identified common themes in the qualitative data, while multiple linear regression and an adaptive lasso analysis extracted key factors from the quantitative data. The findings revealed multiple interrelated individual, contextual, relational, and cultural influences on youth’s engagement. Clinical and research implications are discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".