Postcolonial healing landscapes and mental health in a remote Indigenous community in subarctic Ontario, Canada
Bibliographic record
Abstract
The concept of therapeutic landscape is concerned with a holistic, socio-ecological model of health, but most studies have attempted to explore land-health links from a Western perspective. On an Indigenous reserve in Northern Ontario, part of the Canadian subarctic, we explore the importance of spaces and places in creating postcolonial therapeutic landscapes to treat the wounds inflicted by colonialism. The aim of this research is to gain insight from views and experiences of First Nations residents living on reservations that are undergoing a process of regaining traditional spiritual beliefs and teachings to construct therapeutic spaces to face mental health problems caused by legal opioid analgesic abuse. This qualitative study used semi-structured interviews with Cree and Ojibwe participants to understand how they are reconnecting with earth, spirituality and traditional animist beliefs on their way to recovery. We find that practices such as taking part in ceremonies and ritual spaces, and seeking knowledge and advice from Elders assist with personal healing and enable Indigenous people to be physically and mentally healthy. Our research findings provide important insights into the relationship between space, healing and culture as determinants of health and well-being and document some key factors that contribute to substance abuse recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".