Social Suffering: Indigenous Peoples’ Experiences of Accessing Mental Health and Substance Use Services
Bibliographic record
Abstract
In this paper, we present findings from a qualitative study that explored Indigenous people's experiences of mental health and addictions care in the context of an inner-city area in Western Canada. Using an ethnographic design, a total of 39 clients accessing 5 community-based mental health care agencies were interviewed, including 18 in-depth individual interviews and 4 focus groups. Health care providers also were interviewed (n = 24). Data analysis identified four intersecting themes: normalization of social suffering; re-creation of trauma; the challenge of reconciling constrained lives with harm reduction; and mitigating suffering through relational practice. The results highlight the complexities of experiences of accessing systems of care for Indigenous people marginalized by poverty and other forms of social inequity, and the potential harms that arise from inattention to the intersecting social context(s) of peoples' lives. Service delivery that aims to address the mental health concerns of Indigenous people must be designed with awareness of, and responsiveness to, the impact of structural violence and social suffering on peoples' lived realities. A relational policy and policy lens is key to alleviate patterns of social suffering and counter the harms that are unwittingly created when social suffering is normalized.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".