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Record W3197355453 · doi:10.1177/08862605211037435

Bringing Shame Out of the Shadows: Identifying Shame in Child Sexual Abuse Disclosure Processes and Implications for Psychotherapy

2021· article· en· W3197355453 on OpenAlexaffabout
Rosaleen McElvaney, Rusan Lateef, Delphine Collin‐Vézina, Ramona Alaggia, Megan Simpson

Bibliographic record

VenueJournal of Interpersonal Violence · 2021
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsShameThematic analysisPsychologySexual abuseSuicide preventionPoison controlNarrativeContext (archaeology)DistressPsychotherapistQualitative researchSocial psychologyMedicineSociology

Abstract

fetched live from OpenAlex

Child sexual abuse (CSA) has been described as a highly stigmatizing experience. Despite the recognition of shame as a significant contributor to psychological distress following CSA, an inhibitor of CSA disclosure, and a challenging emotion to overcome in therapy, limited research has explored the experience of shame with young people who have been sexually abused. This study is unique in examining the transcripts of 47 young people aged 15-25 years from a large-scale study conducted in Ireland and Canada and exploring manifestations of shame in CSA disclosure narratives. Using a thematic analysis of both inductive and deductive coding, the data were examined for implicit, as distinct from explicit, manifestations of shame. Three key themes were identified in this study: languaging shame, avoiding shame, and reducing shame. The study supports previous authors in highlighting the need for nuanced measures of shame in research that takes account of the complexity of this emotion. Conceptualizations in the literature of the distinction between shame and guilt are challenged when these emotions are explored in the context of CSA. Finally, recommendations for working therapeutically with young people who have experienced CSA are offered with a view to addressing shame in therapeutic work.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.339
Teacher spread0.307 · 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 designObservational
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

Citations31
Published2021
Admission routes2
Has abstractyes

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