Predicting Risk Behaviors in Adolescents Through Social Isolation and Negative Self-Talk
Bibliographic record
Abstract
Objective: This study aimed to investigate the predictive roles of social isolation and negative self-talk in adolescent risk behaviors. Methods and Materials: The research employed a correlational descriptive design involving 377 high school students from the United States, selected using stratified random sampling based on the Morgan and Krejcie sample size table. Standardized instruments were used to measure the dependent variable (risk behaviors) and the independent variables (social isolation and negative self-talk). Data collection was conducted through self-report questionnaires, and statistical analyses were performed using SPSS version 27. Pearson correlation was used to assess the bivariate relationships between variables, and a standard linear regression analysis was conducted to evaluate the combined and individual predictive power of the independent variables on risk behaviors. Findings: The results revealed significant positive correlations between both social isolation and risk behaviors (r = .41, p < .001), and negative self-talk and risk behaviors (r = .53, p < .001). Linear regression analysis indicated that both social isolation (β = .29, p < .001) and negative self-talk (β = .42, p < .001) were significant predictors of adolescent risk behaviors, with the model explaining approximately 38% of the variance in the dependent variable (R² = .38, F(2, 374) = 113.82, p < .001). Among the two predictors, negative self-talk had the stronger standardized beta coefficient, indicating a higher contribution to risk behavior variance. Conclusion: The findings suggest that both social isolation and negative self-talk significantly contribute to adolescent risk behaviors, with cognitive self-perceptions playing a particularly prominent role. These results underscore the importance of early intervention targeting adolescents’ social connectedness and internal dialogue to prevent engagement in harmful behaviors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".