A Social-Ecological Approach to Addressing Emotional and Behavioral Problems in Schools: Focusing on Group Processes and Social Dynamics
Bibliographic record
Abstract
A substantial body of evidence verifies that social-emotional learning (SEL) can be effectively taught in schools and can reduce the prevalence and impact of emotional and behavioral problems (EBP) among children and youth. Although the positive effects of SEL on individual student’s emotional, behavioral, and academic outcomes have been investigated in some detail in recent years, most studies have focused on evaluating programs aimed at directly training social and emotional competencies with a focus on the individual. Far less is known about the role of interpersonal group dynamics and systems functioning at the levels of the peer group, classroom, and school community. Drawing on Bronfenbrenner’s ecological systems theory and Harris’s group socialization theory, this article reviews the literature on SEL and group dynamics to identify the ways in which existing SEL frameworks already encapsulate social group processes that contribute to the promotion of positive social-emotional development of children and youth. The goals of this contribution are twofold: (a) to document how EBP can be attenuated by addressing group-level processes that already exist within SEL practices and (b) to provide educators with specific SEL strategies to address group dynamics in their classrooms to optimize outcomes for all students, including students with EBP.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".