Bibliographic record
Abstract
It is well established that childhood sexual abuse (CSA) increases the risk for subsequent sexual victimization.However, it is unclear why women with a CSA history may engage in risky behaviours and vulnerability-increasing cognitions that put them at risk for revictimization to a greater degree than women without a CSA history.I proposed and tested a model that uses attachment theory to interpret the increased involvement in risk factors for unwanted sex among women with a CSA history.The proposed model suggests that the extent to which women with a CSA history rely on insecure attachment strategies predicts their involvement in risk factors (sexual activity, substance use, and risk perception deficits) that, in turn, increase the likelihood of unwanted sexual experiences in adolescence and adulthood.Three hundred and eight university women completed measures of childhood and adolescent/adult sexual victimization, attachment strategies, sexual activity, substance use, and risk recognition in a date rape scenario.Among the risk factors assessed, only sexual activity mediated between CSA and unwanted sex.Substance use was not associated with CSA; but it was associated with sexual activity and was a risk factor for unwanted sex.Attachment insecurity was not associated with increased involvement in risk factors, and thus did not mediate the revictimization process as proposed.However, avoidant attachment strategies, especially in the context of low attachment anxiety, were associated with an increased risk for unwanted sexual experiences independent of the other risk factors.Discussion focuses on the potential value of attachment theory as an organizational framework for understanding sexual revictimization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".