Exploring Mental Health and Holistic Healing through the Life Stories of Indigenous Youth Who Have Experienced Homelessness
Bibliographic record
Abstract
Indigenous youth are the fastest growing population in Canada, yet are marked by profound and disproportionate personal, societal, political, and colonial barriers that predispose them to mental health challenges, employment and educational barriers, and experiences of housing insecurity and homelessness. It is only from the perspectives and experiences of Indigenous community members themselves that we can gain appropriate insights into effective supports, meaningful interventions, and accessible pathways to security. This paper will explore the mental health of Indigenous youth who are at risk of, or who have experienced, homelessness, as well as the lifelong perspectives, teachings, and guidance from Indigenous Elders and traditional knowledge keepers; their perspectives are weaved throughout, in order to provide a more effective means to addressing holistic healing and the mental health needs of Indigenous homeless youth. As educators, researchers and clinicians who have sought to understand this issue in more depth, our analysis aims to raise awareness about the complexities of Indigenous youth homelessness and push back against systemic barriers that contribute to homelessness, fail young people, and subject them to oppression. We also offer recommendations from a clinical perspective in order for clinicians, researchers and those working within communities to serve our Indigenous youth with a diverse set of methods that are tailored and ethical in their approach.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".