Practice 9 Trauma-Informed Care: Centring Choice and Connection
Bibliographic record
Abstract
This chapter enhances cultural empowerment and trauma-informed care by centring cultural identities and relationality, culture-centred ways of knowing, and resources and strengths embedded in cultural communities. Melissa, Sandra, and Gwendolyn emphasize the important distinctions between trauma-informed care and trauma-centred practice. Trauma-informed care begins with understanding trauma in the context of sociocultural oppression and marginalization. Historical trauma (as ongoing colonial violence) and intergenerational trauma, for example, result in complex posttraumatic stress for Indigenous Peoples and communities. Other marginalized populations suffer the effects of political violence, including legalized racism. The authors caution that trauma and retraumatization can lead to a loss of cultural identity and relationality, which sometimes manifests in internalized oppression and lateral oppression. Gwendolyn uses the metaphor of a thick fog to describe the impacts of living with historical trauma. Melissa, Sandra, and Gwendolyn position trauma-informed listening as essential to cultural empowerment. Fostering cultural safety, trust, and care requires therapist relational presence and openness. The authors foster mind–body–Spirit–heart strength by honouring client voice and offering tools to foster safety and grounding. Melissa and Sandra have integrated practice illustrations by the following co-authors: • Joanna Gladue (Bigstone Cree Nation) opens the chapter with a Smudge, inviting readers into an ethical space in which Indigenous and western knowledge systems are both honoured. • Judy Chew draws on feminist therapy practices (e.g., collaboration, informed consent, power-sharing, power-analysis) to illustrate how to support clients struggling with internalized oppression to find their own voice. • Zuraida Dada continues her story of apartheid in South Africa as a political and economic strategy grounded in racism and cultural oppression, attending specifically to its intentions and lasting impacts. • Helen Ofosu speaks to the importance of being trauma-informed in the context of the workplace trauma of Black women through persistent or repeated marginalization. She raises awareness of racialized trauma resulting from noninclusive organizational cultures and leadership. • Ruth Strunz critiques the use of applied behavioural analysis in working with autistic clients through the lens of relational trauma. She describes how the intersection of trauma and systemic barriers impacts clients and limits their access to empathetic care. • Aaron Wong unpacks the legacy of racial discrimination within his own family history as Chinese immigrants who faced decades of government oppression. He reflects on the impact of intergenerational loss and trauma on his own health. • Gina Wong joins Sandra and Melissa in speaking to retraumatization as helping professionals. They share examples below of grounding rituals and energy-centred transitions that help them maintain personal well-being while staying present in the therapeutic space.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".