The Impact of Gender, Family Type and Age on Undergraduate Parents’ Perception of Causes of Sexual Abuse
Bibliographic record
Abstract
The purpose of this study was to investigate the Impact of Gender, Family type and Age on undergraduate parents’ perception of causes of child Sexual Abuse. Three hypotheses were formulated and tested. There was a review of relevant literature. The population for the study were 2014 sandwich contact students of Delta State University, Abraka who were about 2000 in number. The sample size of 303 was drawn using the stratified random sampling technique. The instrument for this study was a questionnaire and it had face, content and construct validity from expert judgment and factor analysis. The reliability was assessed with Cronbach Alpha statistics and yielded an r value of 0.90. The data was analysed with t-test statistics and the results revealed that variables of gender and age have no impact on undergraduate parents’ perception of causes of child sexual abuse. But family type has impact on their perception of the causes of child sexual abuse. Recommendations made include the followings: that counselors should organize parents’ conferences and use such fora to enlighten parents on their roles to their children or wards in terms of provision of basic needs as well as supervising and monitoring them, the government should provide students with adequate learning materials so as to reduce the financial burden on parents and thus enhance their care for their children.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".