Estimation of the Predictive Powers of Parental Support and Domestic Violence on Child Abuse Among Primary School Pupils in South-East, Nigeria
Bibliographic record
Abstract
The general purpose of the study was to estimate the predictive powers of parental support and domestic violence on child abuse among pupils in South-East, Nigeria. The study was guided by two research questions and two null hypotheses. The study adopted a correlational survey research design with a population of 481,533 primary five pupils in public primary schools in South-East, Nigeria. A sample of 400 primary five pupils’ was drawn using a multi-stage sampling procedure. Three instruments were used for data collection: Parental Social Support Questionnaire (PSSQ), Parental Domestic Violence Questionnaire (PDVQ) and Child Abuse Questionnaire (CAQ). The reliability of the instrument was established by trial-testing on 30 primary five pupils that were not part of the sample. Cronbach’s Alpha Reliability method was used to determine the internal consistency of the instruments. The reliability coefficients of 0.88, 0.80 and 0.75 were obtained for parental social support questionnaire, parental domestic violence and child abuse questionnaire. Data collected were analyzed using simple linear regression analysis. Specifically, the Coefficient of Determination (r2) was used to answer research questions and Analysis of Variance (ANOVA) aspect of simple linear regression was used to test the hypotheses at 0.05 level of significance. The findings of the study revealed that parental social support significantly predicted child abuse negatively while domestic violence significantly predicted child abuse positively. Based on the findings of the study, it was recommended among others that parents should provide enough social support to their children’s proper development and emotional stability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.001 | 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".