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Record W4402670216 · doi:10.1080/01612840.2024.2396993

Reiterating the Need for Trauma-Informed and Anti-Oppressive Spaces for Disclosure of Sexual Violence: Learning from Those Who Have Lived Through It

2024· article· en· W4402670216 on OpenAlexafffund
Candice Waddell-Henowitch, Deborah McPhail, Christine Kelly, Shawna Ferris

Bibliographic record

VenueIssues in Mental Health Nursing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of ManitobaBrandon University
FundersUniversity of Manitoba
KeywordsSexual violenceLived experiencePsychologyCriminologyDating violenceSocial psychologySuicide preventionPoison controlMedical emergencyMedicinePsychotherapistDomestic violence

Abstract

fetched live from OpenAlex

The social movements of #metoo and #notokay illuminated the extent of sexual violence. By providing a safe platform the movement enabled victims/survivors opportunity to share their own experiences of victimization, often in a delayed disclosure, years after the violence occurred. With new disclosures of sexual violence, also comes a growing awareness of the lasting impact and the requirement to take steps to improve responses to sexual violence across the social and clinical spectrum to address and respond to victims/survivors' holistic needs. The primary research question is, what is the retrospective life experience of individuals marginalized by gender who encounter sexual violence in post-secondary education? The authors of this manuscript used trauma- informed qualitative individual interviews with a feminist perspective to explore the retrospective experience of 10 victim/survivors, a decade or more after their experience of sexual violence. The inquiry discovered the themes of recognizing the wrong, the internal struggle, forging new relationships, and the lasting trauma of sexual violence. Learning from those that lived it legitimizes victims/survivors' experiences and deepens clinical knowledge of these impacts and associated needs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.459
Teacher spread0.384 · 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 teacher head, 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
Published2024
Admission routes2
Has abstractyes

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