Making Safe Spaces for Inuit Youth: The Role of Inuit-led Cultural Programming in Supporting Inuit Youth Mental Wellness
Bibliographic record
Abstract
While there is extensive research on ongoing mental health crises in Inuit Nunangat and how it can be addressed, there is very little academic literature that explores Inuit-led approaches to supporting mental wellness in Inuit communities. This dissertation, in partnership with the Arctic Rose Foundation’s (ARF) Messy Book Program, explores an Inuit-led, arts-based afterschool program that aims to support the well-being of Inuit/Northern Indigenous youth. In doing so, his dissertation seeks to answer: Do Inuit-led youth programs, like the Messy Book Program, contribute to an increased sense of belonging and cultural connection? In turn, do these feelings improve mental health in Inuit youth, and if so, how? This thesis, involving fieldwork in Rankin Inlet, engaged 13 individuals who work(ed) or partner(ed) with the Messy Book Program through in-person and online interviews, as well as nine community members in a focus group, between 2022 and 2024. In building an understanding of the ARF’s programming through these discussions, as well as through ongoing engagement with the ARF, this dissertation outlines the significance, impacts and limitations of four core components of the Messy Book Program: Cultural Cognizance, Safe Space, Youth Mentorship, and Inuit-Led Expressive Arts. These concepts are analyzed through the lens of Inuit Qaujimajatuqangit and Inuit-specific wellness frameworks with the objective to contextualize the Messy Book Program within broader community wellness goals. Central to this thesis, the research partnership with the ARF allowed me unique insight into the growth and adaptation of the Messy Book Program from the start of our research partnership in 2021 through to the conclusion of fieldwork. This thesis therefore captures snapshots of the Messy Book Program at different periods of time – as the program navigated the COVID-19 pandemic, opened (and closed) program sites, and expanded to new regions and Indigenous cultural contexts. Given the fast growth of the program to respond to mental health crises in Inuit and Northern Indigenous communities, the goal of this dissertation is to present these snapshots as a means of positioning Inuit-led programs, like the Messy Book Program, as an integral aspect of community mental health initiatives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".