Betting Addiction Predispositions of In-School Adolescents in Nigeria
Bibliographic record
Abstract
The upsurge of betting and gaming sites on the internet and numerous social networking sites for social activities predisposes adolescents and young adults globally to problem betting including Nigerian secondary school students. In this study, the focus is on the propensities of school-going adolescents becoming addicted as it has become a trend that could have consequences on their mental health and productivity. The study is survey research on a sample of 385 secondary school students and teachers in Owo, Ondo state Nigeria using purposive and simple random sampling techniques. Betting Addiction Tendencies Questionnaire (BATQ)" and a structured interview was used to amass data for the study. The instrument possessed a reliability coefficient of 0.75 after being subjected to the test-retest method. Data were analyzed using percentage, thematic analysis and three-way ANOVA. The study revealed that 40% of the respondents have tendencies for betting addiction. The result further revealed that adolescents with tendencies of betting addiction were characterised with un-serious attitudes and would do anything to get the money to bet. Also, respondents do not differ in their views about betting addiction tendencies based on gender and agebut a significant difference was found based on school type. This study implies that in school, adolescents are susceptible to a betting addiction if unguided. Therefore,Mental Health Counsellors should intensify their effort in providing preventive measures through organizing programmes that could assist in reducing risk factors for betting addiction.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".