Traumatic Experiences and High-Risk Behaviors Among Runaway Youth in Residential Care Centers: The Influence of Sensation Seeking and Impulsivity
Bibliographic record
Abstract
Running away from a residential care center is a worrying reality due to high-risk behaviors adopted by many youths such as delinquency, substance abuse, risky sexual behavior and risky driving. Because youth in residential care may have been exposed to a variety of traumatic experiences (e.g. emotional, physical, and sexual abuse, emotional and physical neglect), the purpose of this study is first, to examine the association between these experiences and high-risk behaviors in adolescents in residential care. Second, this study aims to explore the explanatory mechanisms (impulsivity and sensation-seeking) behind the adoption of high-risk behaviors in runaway and non-runaway youth in residential care. To this end, 125 adolescents aged 15–17 who had run away at least once and 75 who had never run away from their residential care center completed self-report questionnaires about traumatic experiences, high-risk behaviors, sensation seeking and impulsivity. To address the study objectives, correlation and conditional mediation analyses were conducted. Results indicate that sensation seeking influences the relationship between traumatic experiences and delinquency, as well as drug and alcohol use. Thus, addressing sensation seeking tendencies could reduce the risks inherent in the adoption of certain high-risk behaviors among youth in residential care centers.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".