How Children and Young People Disclose That They Have Been Sexually Abused: Perspectives From Victims and Survivors of Child Sexual Abuse
Bibliographic record
Abstract
Child sexual abuse is a pervasive social and public health concern with high social and economic costs. Children and young people who experience this form of abuse are in danger of serious, ongoing impacts on their development, functioning and overall life trajectory as a result of the lasting influence of complex trauma. The recent research, drawing from young adult self‐reports, has found that more than one in three girls and almost one in five boys have experienced child sexual abuse. These alarming figures are not, however, matched by official data reporting rates of substantiated child sexual abuse cases. It is possible that a sizeable proportion of children who have experienced sexual abuse may not be coming to the attention of authorities and, consequently, may not have their needs being met in a timely way, including their need for safety. A question about the disclosure of child sexual abuse emerges, specifically whether, how and to whom children can tell about what has happened or is happening to them. This paper reports on a study focussing on the disclosure of child sexual abuse, based on in‐depth individual interviews with 51 adult victim survivors of child sexual abuse. Findings revealed that most interview participants disclosed a multitude of times before being heard and having their disclosures acted upon. Some were never heard. A thematic inductive analysis is presented and discussed, and recommendations are made for policy and practice reform.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".