Age, Gender, Socio-Economic Status, Attitudes Towards Drug Abuse as Determinants of Deviant Behavior Among Undergraduate Students
Bibliographic record
Abstract
Abstract Evidence from literature shows that deviant behavior is on high side among undergraduates in Nigeria whereas some certain psychosocial factors causing this phenomenon have not been fully explored. This study examined whether age, gender, socio-economic status and attitude towards drug abuse determine deviant behavior among the undergraduate students. It adopted ex-post facto design. Simple random sampling technique was used to sample 269 participants. The Prescription Drug Attitudes Questionnaire (PDAQ) and Deviant Behavior Variety Scale (DBVS) were used to gather data from the participants. T-test analysis and multiple regression were used to test the formulated hypotheses. The results revealed that age, monthly allowance and attitude towards drug abuse have significant joint prediction of deviant behavior (R=.358 R2 =.128, F=10.594, p<.05) while only attitude towards drug abuse independently predicted deviant behavior (R=.236, R2 =.056 F = 17.112; p<.01). Also, there was a significant gender difference on deviant behavior [t (293) = 4.196, p<.01], where male respondents scored high significantly (M=4.09, SD=3.44) compared to female respondents (M=2.53, SD=2.31) on deviant behavior. It is therefore recommended that policy makers in educational sector and the governing council of tertiary institutions need to create awareness on the debilitating effects of drugs on students’ behavior, especially among males.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".