Influence of Social Media on Sexual Behaviour of Youth in Kwara State, Nigeria: Implications for Counselling Practice
Bibliographic record
Abstract
This study investigated the influence of social media on the sexual behaviour of youth in Kwara State. Descriptive research design was adopted for the study. A total of 395 youth participated in the study. One research question was raised while three null hypotheses were formulated and tested at 0.05 level of significance. The instrument used for data collection for this study was a researcher-designed questionnaire entitled “Influence of Social Media Questionnaire” (ISMQ). The findings revealed that social media has considerable influence on the sexual behaviour of youth in Kwara State. Social media leads students to the act of sending erotic messages, watching pornographic films and movies, and also increases risky sexual behaviour such as masturbation. There were no significant differences in the influence of social media on sexual behaviour of youth in Kwara State based on gender, age and university attended. It was therefore recommended that counsellors should expose students to the danger inherent in negative uses of social media and analyze the possible result of proper usage of social media. Counsellors should also provide information specifically on the safe and respectful use of technology, as well as consequences of the negative use of social media to students of different genders, ages and universities attended.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".