Children and Violence: Nurturing Social-Emotional Development to Promote Mental Health
Bibliographic record
Abstract
Abstract The absence of violence against children is a fundamental children’s right and a major milestone of civilized society. Similarly, reports on incidences of violence by children and youth, including severe cases with devastating consequences, speak to the need that the trauma of exposure to violence in childhood needs to be addressed. While violence and its risk factors are generally understood, what is less clear are the essential protective factors, how we can identify those as early as possible, and how we can use them to prevent and address the trauma of violence exposure in children and youth. In this report, I review pathways of child and youth violence through the lens of social-emotional development as a central protective factor. Negative emotions of frustration and anger can underlie violence and aggression. Kind emotions, such as caring and our ability to connect with others emotionally, can serve as social-emotional protective factors. A brief review of the central social-emotional processes and their development is provided, including the human capacity to feel with others and express empathy, be emotionally aware and care about the effects of one’s own actions on others, and be able to regulate the self and their emotions. Given the negative widespread and long-term impact of exposure to violence, I describe research-informed attempts to prevent violence exposure across development. Taking a humanistic, strength-based perspective, the focus is on social-emotional protective factors to address violence and nurture mental health in every child. I conclude with recommendations for practice and policy.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".