Strengths Moderate the Impact of Trauma on Risk Behaviors in Child Welfare
Bibliographic record
Abstract
Objectives: To determine whether traumatic experiences of children entering the child welfare system have an impact on their risk behaviors and whether these behaviors are moderated by children's strengths. Method: The Illinois Department of Children and Family Services administered the Child and Adolescent Needs and Strengths (CANS) measure to 8,131 children as they entered custody and analyzed Traumatic Experiences, Risk Behaviors and Strengths using polytomous logistic regression models. Results: Children entering child welfare have suffered multiple traumatic experiences. There is a strong linear relationship between the number of these experiences and the level of the children's high risk behaviors. However, there is an interactive effect between traumatic experiences and children's strengths on the risk behaviors, with strengths having a greater moderating effect as the number of traumatic experiences increases. Conclusions: Children entering the child welfare system present with complicated histories that include multiple traumatic experiences and multiple high risk behaviors. However, the more strengths these children have developed, the less likely they are to engage in high-risk behaviors. This resilience has major implications for both prevention and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".