Empathy and Sexism as Predictors of Childhood Sexual Abuse Myths in University Students
Bibliographic record
Abstract
Childhood sexual abuse (CSA) is one of the situations that can negatively affect the emotional, mental and social life of the child. Myths that determine adults' perspectives on CSA may cause the child to experience a new trauma after sexual abuse. Therefore, the purpose of this study was to investigate whether sexism and empathy variables predict childhood sexual abuse myths and whether CSA myths differentiate based on gender. Participants consist of students of a state university in Turkey. In this study, Toronto Empathy Questionnaire, Ambivalent Sexism Inventory, and Childhood Sexual Abuse Myth Scale were used to collect data. Multiple regression analysis method and independent samples t test were used for statistical analysis. Multiple regression results show that there is a meaningful relation between CSA myths and sexism (benevolent and hostile dimensions) and empathy variables (R = .36, R2 = .13, p = .00). The combination of sexism and empathy variables explains 13% of total variance in students’ CSA myths. Moreover, in this study, it was determined that women had fewer myths than men. These results suggest that prevention studies at an individual level are not sufficient to prevent child sexual abuse or treat victims appropriately, and it proves that studies on a social level is absolutely necessary. In this respect, further studies may examine the effects of trainings about these variables on the embracing CSA myths.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".