Relationship between Child Sexual Abuse, Psychiatric Disorders and Infectious Diseases: A Matched-Cohort Study
Bibliographic record
Abstract
Child sexual abuse (CSA) has been strongly associated with a range of psychological and physical problems in childhood and adulthood, such as anxiety, post-traumatic stress disorder (PTSD), and infectious diseases. Despite the strength of these associations, no studies to date have investigated psychobiological processes that might underlie the relationship between CSA and physical health problems occurring during childhood, such as infectious diseases. The goal of the current study is to evaluate PTSD as a potential mediator between CSA and the occurrence of infectious diseases among children and adolescents. Furthermore, we postulate that PTSD plays a specific role as an indicator of chronic stress during childhood, in comparison to other mental disorders, such as anxious and non-anxious disorders (e.g., depression). Via a prospective matched-cohort design, administrative data were used to document PTSD, anxious and non-anxious disorders, and infectious diseases. The sample size was 882 persons with a substantiated report of sexual abuse and 882 matched controls. Negative binomial regressions revealed that CSA is associated with a greater number of anxious diseases diagnoses that, in turn, predict more infectious diseases diagnoses. These findings highlight the importance of preventing and intervening among sexually abused youth with anxious disorder symptoms to limit negative outcomes on physical health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".