Characteristics of first disclosure of child sexual abuse: age, delay, recipient, and feeling of support
Bibliographic record
Abstract
BACKGROUND: Little population-based evidence exists about the characteristics of first disclosure of child sexual abuse (CSA), including age and delay. OBJECTIVE: To generate population-based evidence about the age of first disclosure, delay to first disclosure, first recipients, and feelings of support. PARTICIPANTS AND SETTING: The Australian Child Maltreatment Study collected information about CSA victimization from a nationally representative sample of 8503 individuals aged 16 and over. CSA prevalence was 28.5 %. Almost one half of these individuals (45.2 %) had never disclosed their experience of CSA before participating in the survey. Slightly over one half (54.8 %) of these individuals had disclosed before participating, and provided information about the characteristics of their first disclosure. METHODS: We generated national estimates for age of first disclosure, delay to first disclosure, first recipients, and feeling supported, and compared results by gender and age group. RESULTS: Among individuals who disclosed, 70.2 % first disclosed before age 18, comprising more women (73.7 %) than men (60.4 %); and with participants aged 16-24 more likely (81.2 %) than people aged 25-44 (67.8 %) and 45 or more (68.7 %). Among individuals who disclosed, almost half (45.6 %) first disclosed within a year; the median delay was 1 year and the mean delay was 7.1 years. One in 10 people aged 25 or more (11.1 %) delayed more than 20 years. First recipients were typically mothers (30.9 %) and friends (24.9 %). Most people felt supported (79.8 %), especially those aged 16-24 (87.3 %). CONCLUSIONS: We identified significant trends in CSA disclosure characteristics. Many people first disclose in childhood, often within a year. Yet, many only disclose in adulthood, after a long delay. While disclosure is becoming more common, measures are required to facilitate disclosure and supportive responses. Findings have implications for health, education and legal systems.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".