State-sanctioned Violence and Mental Health: Implications for Learning and Treatment
Bibliographic record
Abstract
This qualitative study examines how Canadian mental health systems may better accommodate people from the Democratic Republic of the Congo (DRC) who are living now in Canada and who may be suffering psychosocial consequences of political violence. Using an anti-racist-feminist lens, analysis of seven interviews highlights the significance of socio-political histories and conceptualisations of trauma in mental health education. Discussions focus on mental health dimensions of war and aspects of resilience in complex life-experiences which are only recently gaining representation (and transformation) in medical discourse. By correlating social and political forces with particular mental health outcomes, I focus on treatment-areas that may be discriminatory for a subset of refugees.As immigrant and refugee experiences may lack reference within traditional treatment paradigms, it becomes essential to understand specificities within which healing and social change for refugees may be relevant. A first objective looks at processes of resilience where recovery may be more meaningful, countering notions of illness as pathology within systems of deficit. Similarly, greater considerations of state-sanctioned violence, its nature and intentions, are needed to plan comprehensive mental health supports. A second objective is then to identify social indicators which may more fully articulate, express, or enable change at group and family levels. Finally, as interventions are most effective when informed by group realities, a third objective brings forward cultural, political, and social forces currently underrepresented in what we know about `treating war trauma'. In sum, this study highlights that greater attention to structural violence in mental health education may lead to better treatment outcomes. Treatments may be more effective by offering alternate pathways to care, considering broader conceptualisations of trauma, and encouraging notions of participatory, group-level initiatives. Findings critically suggest that present mental health systems may unintentionally (re)produce victims, more than enable survivors to rebuild lives after fleeing the complexity and brutality of war-affected circumstances.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.059 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".