Improving indigenous mental health care based on the First Nation’s Mental Wellness Continuum Framework
Bibliographic record
Abstract
Background: Indigenous Canadians are more likely to suffer from both physical and mental \nillnesses as compared to the general population (Nelson & Wilson, 2017). This multifaceted \nissue is attributed to socio-economic disparities that are a direct result of the historical impacts of \ncolonialism. Indigenous Canadians continue to experience negative consequences of colonialism, \nas well marginalization and discrimination, especially when navigating the health care system. \nThe current biomedical model fails to meet the mental health needs of Indigenous people. The \npurpose of this practicum is to develop an educational workshop to provide to front-line home \nsupport staff with the knowledge and skills to optimize Indigenous mental health care delivered \nthrough the Nunatsiavut Government’s home support program. Methods: The methods, guided \nby Canadian First Nation’s Mental Wellness Continuum Framework by Health Canada (2015), \nincluded an integrative review on Indigenous mental health care in community settings, and an \nin-depth consultation with management, home support staff, and clients, for the purposes of \nobtaining broad local perspectives. Results: Key results from the integrative review included: \nthe importance of practicing cultural safety and holism during care provision, maintaining \nworker wellness through self-care, the development of therapeutic relationships between \nproviders and clients, and self-determination for Indigenous care-recipients. The consultation \nrevealed many positive impacts the home support program currently has, additional educational \nneeds for staff, cultural care needs for clients, and various traditional self-care techniques. \nConclusion: The framework, integrative review, and consultations were all integral components \nin the development of this educational workshop, which seeks to engage and empower home \nsupport staff to deliver optimal mental health care that is both fulfilling to both the provider and \nclient.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".