Community-Based Mental Health Initiatives in a First Nations Health Centre: Reflections of a Transdisciplinary Team
Bibliographic record
Abstract
As a case study on collaboration, this paper is a first person account from a psychologist and a social worker and their experiences developing and piloting community- based mental health programs for a rural Albertan Cree community. We provide an overview of two such pilots, the Family Wellness Program and the Community-based Anger Management Workshops. Here we reflect on our attempts to integrate mental, physical, emotional, and spiritual considerations consistent with the community cultural context. Each of these programs have been developed and offered within both interdisciplinary and multidisciplinary contexts involving counsellors, teachers, nurses, and community Elders from within and outside the community. Such dynamic programming has evolved into transdisciplinary community-based mental health initiatives that have enhanced community wellness but also taxed these rural service providers. Together we share our reflexivity, outlining some of the issues, challenges, and inspirations in our separate and collaborative work in our attempts to foster mental health and community wellness for this resilient but marginalized population. Providing culturally responsive clinical services within a community mental health model requires a solid understanding of the social forces and con- text shaping our lives and the lives of the commun- ities we serve. Key competencies for culture-centred services include: self-awareness, understanding the world view of our clients, using appropriate inter- vention skills and techniques, and involvement in organizational developments (Arthur and Stewart, 2001). This case study sharing is a first person ac- count of the experiences of two professionals work- ing together as agents of change. With that in mind, we wish to begin with a brief personal placement. Judi is a Canadian woman who lives and works in her lifelong rural community of St. Paul, Alberta. She is an academic and practising registered psych- ologist who has travelled to work in Frog Lake First Nation for seventeen years. Darrell is a Cree man and proud member of Frog Lake First Nation, a small rural Aboriginal community in Canada. He lives off reserve and travels to his home community to provide services as a registered social worker. We have worked separately and collaboratively in this community. Here, we want to share our experiences developing and piloting community-based mental health programs. We have sought the guidance and permission of community Elders, specifically Francis Quinney and Doris Okanee, who, as gifted helpers and respected Cree Elders, consented to our shar- ing these stories and encouraged us to publish this article as a teaching tool. The programs we highlight here arose out of dis- satisfaction with existing services which tended to
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.060 | 0.029 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".