Moving Toward Wellness: A Transformative Approach to Addressing Inadequacies in Mental Health Supports for Indigenous Youth in Residential Treatment Resource
Bibliographic record
Abstract
Indigenous youth are overrepresented in the child welfare system in Canada. Organization X, a Ministry of Children and Families program provider, has made significant efforts to address the needs of Indigenous youth in care, including creating the Residential Treatment Resources (RTR) program. While behavioural-focused RTR program is an important step in the right direction, the program fails to address the causal mental health needs, which has resulted in high numbers of recidivism among Indigenous youth after discharge. After careful review of the literature, the possible solutions revealed mental health counselling is a vital supportive resource required for this population. Additionally, counselling must be culturally sensitive, include traditional practices, be client centred, and be collaborative with both the youth and the Indigenous department, which is a part of Organization X. The lens of this organizational improvement plan (OIP) is transformative and centres on marginalized Indigenous youth. The theoretical lens that works in conjunction with this is critical race theory, which examines race, racism, and power. Specifically brought to bear is the critical Indigenous research methodology. The paradigm and theoretical perspective complement the two leadership approaches that will guide the change. Transformative leadership and distributive leadership will motivate and empower stakeholders to actively and enthusiastically engage in the change process. The change implementation plan draws from and is guided by the four steps of Deszca et al.’s (2020) Change Path Model: awakening, mobilization, acceleration, and institutionalization. Also presented in this OIP are plans for monitoring, evaluating, and communicating the change process.
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 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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".