Beyond medicalized approaches to violence and trauma: Empowering social work practice
Bibliographic record
Abstract
Summary Social workers are positioned to respond to clients with a history of trauma by practicing bio-psycho-social, trauma, and violence-informed care but frequently encounter systemic barriers to providing holistic care. The research presented in this article was initiated by a College of Social Work in Canada in response to concerns raised by social work providers that their practice was constrained by ideological, structural, and system limitations within publicly funded mental health and addiction services. Ideologically trauma-based social work care is defined by five principles of safety, trust, collaboration, choice, and empowerment and recognizes that what has happened to individuals, including early adversity, can influence their bio-psycho-social functioning across the lifespan. Structurally, trauma-based care recognizes the corrosive impact of poverty, systemic discrimination, and exclusion. Findings Our research included a literature review, an online survey (n = 115 completed surveys), individual interviews (n = 50), and three focus groups (n = 15). The findings consistently highlighted a dissonance between dominant bio-medical approaches and reliance on the Diagnostic Statistical Manual of Disorders and social justice–based practice. Primary themes included a recognition by social workers of the pervasive presence of trauma and its effects, including mental health and addiction challenges; intergenerational impacts of trauma; the limitations of the medical model; and the need to reposition social work practice. Applications Repositioning the role of social work within mental health and addiction settings to center social justice responses to trauma presents transformative opportunities to better meet the needs of service users and increase workplace satisfaction.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".