Prevalence and correlates of the perpetration of cyberbullying among in-school adolescents in Oyo State, Nigeria
Bibliographic record
Abstract
OBJECTIVE: Cyberharassment/cyberbullying is a global problem that has been inadequately investigated in developing countries. In this paper, we present findings on the prevalence and predictors of perpetration of cyberbullying among in-school adolescents in Oyo state, Nigeria. METHODS: A total of 653 students were selected via multi-stage sampling. Information on history of perpetrating harassment via an electronic medium in the 3-month period preceding the survey was obtained. RESULTS: Respondents' mean age was 14.2±2.2 years and 51.3% were females. All respondents had personal mobile phones and about half had Internet access. About 40% accessed the Internet every day while about 48% accessed it at least once to several times a week and <5% accessed it about once every 2 weeks. One hundred and fifty-six (23.9%) had harassed someone electronically, 260 (39.8%) had been victimized, and 137 (21.0%) were both victims and perpetrators. Common modes of harassment were via phone calls 99 (63.5%), chat rooms 70 (44.9%), and text messages 60 (38.5%). Students who had been victims of cyberbullying (OR=21.76, 95% CI=12.64-37.47) and those with daily Internet access (OR=2.32, 95% CI=1.28-4.19) had significantly higher Oods of being perpetrators. CONCLUSION: About a quarter of students were perpetrators of cyberbullying, and the correlates of perpetration were history of cyber victimization and daily Internet access. Intervention programs must be instituted for victims as well as frequent users of the Internet to curb the problem in the study area.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".