“The land is a healer”: Perspectives on land-based healing from Indigenous practitioners in northern Canada
Bibliographic record
Abstract
This research paper articulates a largely undefined cultural concept within mental health promotion and intervention, described as ‘land-based’ healing, which has been understood and taught for millennia by Indigenous knowledge holders. This knowledge is currently being revitalized by northern practitioners where ‘land’ is understood as a relational component of healing and wellbeing. Land-based activities such as harvesting, education, ceremony, recreation, and cultural-based counselling are all components of this integrative practice. Land-based practices are centered in Indigenous pedagogy and recognize that cultural identity is interwoven with and connected to ‘land.’ Directly cultivating this fundamental relationship, as assessed through a culturally relevant lens, increases positive mental health and wellness outcomes in Indigenous populations. In this study, qualitative narrative methods were used to document the experiences of eleven land-based program practitioners from the three northern territories in Canada. As experts in this field, practitioners’ narratives emphasized the need for a greater understanding and recognition of the value of land-based practices and programs within mainstream health. The development of working definitions, terminology, and framing of land-based practice as a common field are delineated from relevant literature and practitioner narratives in order to enable cross-cultural communication and understanding in psychology. Land-based healing is presented as a critical and culturally appropriate solution for mental health intervention and community resilience in northern Canada.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.051 | 0.023 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".