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Record W7155537911 · doi:10.71446/hs57131297

Practice 9 Trauma-Informed Care: Centring Choice and Connection

2025· book-chapter· W7155537911 on OpenAlexaff
Melissa Jay, Sandra Collins, Gwendolyn D. Villebrun, Joanna Gladues, Judy Chew, Zuraida Dada, Helen Ofosu, Ruth M. Strunz, Aaron Wong, Gina Wong

Bibliographic record

Venuenot available
Typebook-chapter
Language
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsUniversity of AlbertaAthabasca University
Fundersnot available
KeywordsOppressionIndigenousEmpowermentPoliticsColonialismHistorical traumaCultural identityIdentity (music)Metaphor

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.038
Scholarly communication0.0120.013
Open science0.0030.018
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0210.005

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.

Opus teacher head0.020
GPT teacher head0.316
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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