Bringing Shame Out of the Shadows: Identifying Shame in Child Sexual Abuse Disclosure Processes and Implications for Psychotherapy
Bibliographic record
Abstract
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.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".