Book Review: <i>Healing Traditions: The Mental Health of AboriginalPeoples in Canada</i> Edited by Laurence J. Kirmayer andGail Guthrie Valaskakis
Bibliographic record
Abstract
In Healing Traditions, the editors have assembled the voices of 29 academics, researchers, and mental health professionals from across Canada as well as Australia and the United States. This distinguished panel offers an important contribution to our understanding of Aboriginal mental health issues and the unique healing processes currently underway in a number of communities. Kirkmayer and Valaskakis contextualize mental health in a distinctive manner, acknowledging how Canada’s First Peoples have been affected by colonization over several hundred years. We learn how historic social policies continue to affect individuals, their families, and the communities in which they live. The notion of identity and the social confusion these policies create are developed in several chapters. The heterogenic nature of these communities, with their own cultural values and experiences requiring distinct healing strategies for Canada’s Métis, Inuit, Cree, or other Indigenous communities, is elaborated on by the contributors. These themes are intertwined within each of the book’s sections, but rather than causing confusion, their reiteration reinforces the concept that practitioners must be students of history as well as students of their field of practice in order to engage effectively with Aboriginal people and their healing pathways.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".