Decolonising Trauma Work: Indigenous Practitioners Share Stories and Strategies
Bibliographic record
Abstract
This dissertation explores the areas of healing and wellness within Indigenous communities on Turtle Island. By drawing on a decolonising approach to Indigenous health research, this study engaged 10 Indigenous healthcare practitioners in a dialogue regarding Indigenous worldviews; notions of wellness and wholistic health; critiques of psychiatry and psychiatric diagnoses; and the cultural strategies that Indigenous healthcare practitioners utilise while helping their clients through trauma, depression, and experiences of “parallel and multiple realities.” Importantly, this study addresses a gap in literature and puts forth a necessary contribution in regards to Indigenous peoples and psychiatry. Indigenous healthcare practitioners reveal their thoughts and strategies in relation to psychiatric diagnoses, cultural treatment, and psychotropic medication. The stories and strategies gathered during the interview dialogues created a broader discussion that is situated among the existing literature. This research found that Indigenous knowledge and experience was deeply embedded in the practises of Indigenous healthcare practitioners. The strategies presented by these practitioners offer purposeful and practical methods that originate from Indigenous worldviews, yet can be utilised by any practitioner that is seeking therapeutic strategies to help traumatised individuals and communities. Moreover, this research will be a particularly relevant resource for health policy initiatives, agency programming, and education and training institutes. Bringing forth Indigenous strategies for helping and healing is a vitally important contribution to the field of trauma work.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.032 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| 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".