A Meta-Analysis: The Effects of Child Sexual Abuse Towards Children
Bibliographic record
Abstract
Recently, cases of sexual abuse against children became a phenomena and it is a traumatic events that give a deep impact towards the victim. Therefore, the purpose of this study was to conduct a meta- analysis on a published researched about effects of child sexual abuse towards children. There were 20 journal articles collected from Science Direct, SpringerLink, Willey Online Library and Web of Science databases with the keywords ‘child sexual abused’ ‘behavior’, ‘emotional’, ‘social’ and ‘academic’ being used. The year of papers selected were from 2010 until 2017. The researcher differentiated and analyzed the effect of child sexual abuse toward four themes: internalizing and externalizing behavior, emotional regulation, suicidal behavior and academic achievement and performance. The findings from this study shown that child with previous experiences as victims in child abuse display internalizing and externalizing behavior and poor on academic achievement and performance. The findings also shown that the sexually abused children especially girls has low emotional regulation and the victims also had suicidal ideation and suicidal attempt. The implication from this study was to provide the insight for future researchers on the effects of child sexual abuse in behavior, emotional, social and academic aspects. Since this study only focused on the effect of sexual abuse towards children, it was suggested that for future researches, the effects of sexual abuse towards different range of age such as adult with history of sexual abused should be further investigated and more effects apart from behavioral, emotional, social and academic should be considered.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".