An examination of adolescent engagement in risky behaviours: assessing predictors and intervening in schools
Bibliographic record
Abstract
Adolescents may engage in risky behaviours as an attempt to manage negative affect and stress during a difficult developmental period, yet using such a maladaptive coping strategy comes at a cost.Given the important potential negative long-term consequences of engaging in risky behaviours, the high prevalence in adolescence, and the clinical implications, there is a need to delineate reliable vulnerability factors, as well as designing and implementing intervention programs.This dissertation is comprised of three manuscripts that collectively contribute to the literature by documenting: (1) personal and environmental factors associated with adolescent risky behaviour engagement; (2) the relationship between different executive function skills and adolescent broad-based engagement in risky behaviours; and (3) the effectiveness of an in-school intervention for adolescents designed to target emotional regulation skills related to risky behaviours.The current research examines adolescents' engagement in risky behaviours in an attempt to identify predictive factors and reduce such engagement through intervention.The three manuscripts are unique as they are the first exploratory examinations of general personal and environmental factors and various executive function skills in relation to broad-based engagement in risky behaviours.Further, the third manuscript is the first attempt to design, implement, and examine the potential benefits for reducing risky behaviours by intervening on a known vulnerability factor.The first manuscript reports on 160 adolescents (46% male and 54% female) between the ages of 12 and 18 (M = 15.17;SD = 1.22) and examined whether personal (i.e., intrapersonal, temperament, symptoms, and coping) and environmental (i.e., interpersonal and negative life events) factors are associated with risky behaviour engagement.Results of the first study indicate that personal factors account for a greater proportion of the variance in risky behaviour engagement as compared to environmental factors.However, while a number of personal factors (i.e., impulsiveness, low anxious symptoms, and poor self-concept clarity) predict adolescent engagement in risky behaviours, the strongest single predictor of risky behaviours is negative life events (i.e., an environmental factor).Furthermore, age-related comparisons indicate that older male adolescents are most likely to engage in risky behaviours.The second manuscript examined broad-based engagement in risky behaviours and the predictive power of different measures of executive function skills among 102 adolescents (48% male and 52% female) between the ages of 12 and 19 (M = 15.07,SD = 1.53).Results indicated that adolescents who exhibited low overall scores on observer-reported executive function were more likely than adolescents who exhibited high levels of executive function to engage in risky behaviours.However, there was no relationship between the performance-based measure of adolescent executive function and risky behaviours.The third manuscript included 41 adolescents (71% male and 29% female) between the ages of 12 and 17 (M = 14.2,SD = 1.4), and examined the efficacy of a pilot program (i.e., Cognitive Emotion Regulation Training Intended for Youth) to improve cognitive emotion regulation, and reduce subsequent engagement in risky behaviours.Participants made significant gains with regard to using adaptive cognitive emotion regulation strategies (e.g., positive reappraisal and refocusing on planning), yet no benefits were found for reducing maladaptive cognitive emotion regulation strategies or risky behaviours.Taken together, findings from these three studies provide insight into vulnerability factors and intervention for adolescent risky behaviour engagement.Also discussed are the implications of this research for school psychologists who work with adolescents who engage in such maladaptive behavioural patterns.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".