Is there a positive side to sensation seeking? Trajectories of sensation seeking and impulsivity may have unique outcomes in young adulthood
Bibliographic record
Abstract
INTRODUCTION: High levels of sensation seeking and impulsivity in adolescence are typically associated with risky behaviours; limited research has examined the relation of these traits to positive outcomes. Given that adolescence is a sensitive developmental period that can impact success later in life, we adopt the Positive Youth Development Framework to better understand how the development of self-reported sensation seeking and impulsivity may be differentially related to positive markers of early adulthood. METHOD: Data are from the Victoria Healthy Youth Survey (T1 N = 662; 52% female), a six-wave longitudinal cohort study of Canadian youth. Parallel process latent class growth analysis estimated trajectories of sensation seeking and impulsivity identifying classes of youth (ages 14-28). Controlling for baseline age, sex, and socio-economic status, linear regression analyses examined how longitudinal patterns (classes) of sensation seeking and impulsivity were related to positive markers of early adulthood. RESULTS: Three classes of youth were identified. These varied in levels and trajectories of change in sensation seeking (Ss) and impulsivity (I): LowSs-LowI, 26%; HighSs-HighI, 35%; ModerateSs-LowI, 38%. In young adulthood (T6; ages 22-29), youth in the LowSs-LowI and ModerateSs-LowI classes had significantly higher educational and occupational achievement, and lower financial strain, compared to youth in the HighSs-HighI class. Further, the ModerateSs-LowI class was associated with the highest levels of income and well-being. CONCLUSIONS: Findings identified differential trajectories of sensation seeking and impulsivity, with youth in the ModerateSs-LowI class, followed by the LowSs-LowI class, reporting the most positive outcomes in young adulthood.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".