Therapeutic Strategies (Resiliency & Self-Control) in Reducing Late Adolescents’ Risky Smoking Behaviour in Oyo State, Nigeria
Bibliographic record
Abstract
Cigarette Smoking is one of the risky behavioural problems of exploration and experimentation of theadolescents. One of every six Nigerian adolescents is a smoker, hence the adolescents need help. This studyinvestigates the efficacy of resiliency and self-control strategies on the management of late adolescents’ riskysmoking behavior. The study adopted experimental design with a 3x2 factorial index. 120 senior secondaryschool students were randomly selected from three secondary schools, consisting of only male students withdiverse characteristics. The treatment lasted for eight weeks. Adolescents Smoking Behaviour AssessmentQuestionnaire was used for the study. Statistical Package for Social Sciences (SPSS) was adopted for thereliability with internal consistency of 0.95 (r=0.95). The choice of items and its selection determined the contentvalidity, with 25 items finally used. Two hypotheses were postulated and tested at 0.05 level of significance. Thefindings reveal that self-control strategy significantly affect smoking behaviour of the late adolescents (F(1,39)=18.103, P<0.05). Also resiliency strategy significantly affect the smoking behavior of the late adolescents(F(1, 39) =15.883, P<0.05). The two strategies are effective in the management of late adolescents’ risky smokingbehaviour. The duo can always complement each other in treatment.
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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 | 0.000 | 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) | 0.001 | 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".