“How many times did I not want to live a life because of him”: the complex connections between child sexual abuse, disclosure, and self-injurious thoughts and behaviors
Bibliographic record
Abstract
BACKGROUND: Meta-analyses have confirmed an association between child sexual abuse (CSA) and non-suicidal and suicidal self-injurious thoughts and behaviors (SITB), yet the mechanisms linking these factors are, to date, poorly understood. The goal of the current study is to explore one potential influencing factor acting in the association between CSA and SITB, which is the disclosure experience. Disclosure has been identified as a prominent factor in the healing process of survivors, with a lack of support following disclosures heightening negative outcomes. Exploring the impact of CSA disclosure on SITB is necessary to build effective prevention and intervention strategies. METHODS: This qualitative study is part of a larger initiative spanning diverse research sites in Canada and in Ireland and aiming to lend voice to young people who were sexually abused in childhood/adolescence. Participants were recruited from community-based sexual abuse/assault agencies, hospital-based specialized clinics and child advocacy centres. The Long Interview Method, based on a branch of phenomenology, was used to guide research design and data collection. The current thematic analysis, informed by a stress-diathesis model, is based on a sample comprised of 21 ethnically diverse youth aged 15 to 25 who self-reported a sexual abuse experience in their childhood or teenage years and who, as part of the interview on their disclosure processes, revealed past or current SITB. RESULTS: The thematic analysis led to the identification of four main themes that both confirmed past research and conceptual models on SITB, and provided new insights. Participants perceived a clear link between their CSA experience and SITB and other mental health issues. They offered their views on the meanings of SITB for CSA victims: to cope with abuse; to end the abuse; to express self-hatred and loneliness; and to let people know about their suffering. They described how negative disclosure experiences led to more nonsuicidal and suicidal SITB. Yet, participants also revealed that receiving support for their SITB created opportunities for CSA disclosure and support. CONCLUSIONS: This study showed complex connections between CSA experiences, disclosure and nonsuicidal and suicidal SITB. Understanding the reciprocal influences between SITB, CSA disclosure and help-seeking could better equip mental health professionals and caregivers to provide support and foster healing and recovery in CSA victims.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".