PROMOTIVE FACTORS DURING ADOLESCENCE: ARE THERE CHANGES IN IMPACT AND PREVALENCE DURING ADOLESCENCE AND HOW DOES THIS RELATE TO RISK FACTORS?
Bibliographic record
Abstract
<p>In this study we compared the impact of promotive and risk factors on 18-month recidivism during adolescence. <em>We measured bipolar factors (factors with risk and promotive effects being the ends of the same continuum) for 13,613 American juveniles who had committed a criminal offense. For most of the factors </em>no significant differences were found between the impact of the promotive and risk ends. Interventions aimed at increasing promotive factors may therefore be potentially just as effective as interventions aimed at decreasing risks. The importance of both promotive and risk factors was found to be significantly higher for younger than for older adolescents in almost every domain, which emphasizes the importance of early intervention. Furthermore, age and sex differences were found in the impact and prevalence of promotive and risk factors.<em> </em>The discussion focuses on implications for clinical practice.</p>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".