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Record W7009784639

A feminist post structural analysis of trauma informed care policies in BC

2021· dissertation· en· W7009784639 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)Subject (documents)Power (physics)Economic JusticeIntersectionalityOntologyFeminismPoliticsPrecarityDiscourse analysis
DOInot available

Abstract

fetched live from OpenAlex

My study examines trauma informed practice (TIP) policies in BC, Canada. My chosen methodology, what is the problem represented to be (WPR) (Bacchi 2009), makes politics visible in policies. I am interested in the effects of trauma policies on women who experience male violence. How does discourse produce certain effects and constitute specific subjects within these texts? I extend a politicized analysis of TIP policies, specifically, an in-depth feminist post structural analysis. I advance an understanding of the effects of policy, particularly for women who have experienced male violence and who receive services under the TIP guidelines. I note the absence of an intersectional analysis and the lack of attention paid to power relations, specifically associated with the provision of care within the health care system, the construction of the traumatized female subject and the absence of a social justice lens in TIP policies. My study addresses the meanings, and resulting practices arising from the TIP policy and its impacts on women's lived experiences. My feminist post structural analysis provides a critique of TIP policies glaringly absent from the literature. I examine available literature, which evaluates TIP. My analysis deepens the understanding of the policy's inherent assumptions by revealing the problem of trauma, as represented in TIP policies. I explore the emergence of the dominant concept of trauma in the completion of a genealogy of trauma. I uncover the commonly accepted trauma ethos, a set of principles and beliefs about violence against women that has set the path for a trauma discourse in BC's guidelines, policies, and programs. I explore my interest in iv the ontology of trauma, the nature of trauma itself and the way of being when trauma has occurred. While exploring this interest through a genealogy of trauma, I identify five historical figures; the traumatized female figure, the assaulted woman figure, the wounded veteran figure, the colonized Indigenous woman figure and the emancipated woman figure. My study explores how women are obscured and invisible in policies intended to address violence against women. I demonstrate that this invisibility results in gender-neutral policies-if there is no gender-based violence- we, therefore, do not have to think of gender-based treatment. The patriarchal erasure of women from trauma policies continually repositions what the problem is represented to be. These policies constitute women as the less valued subjects, fundamentally damaged and flawed. Trauma policies shape women as people who can damage staff; assuming they are a source of trauma infection; they can infect staff with their trauma resulting in vicarious traumatization of staff. Trauma policies characterize the traumatized female subject as fundamentally different from the staff or the professional expert. Only certain kinds of women can be traumatized, the mentally ill and substance-using women. My study exposes the presupposition embedded in policies that only certain women are violated, and other women are unlike them. This trauma discourse is grounded in racism, colonialism and sexism, built on stereotypical patriarchal representations of women, resulting in the stigmatization of women who experience male violence.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.246
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0190.020
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0110.000

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.

Opus teacher head0.023
GPT teacher head0.324
Teacher spread0.301 · 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 designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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