Social Emotional Learning Strategies for Students in Self-Contained Classrooms: A Systematic Review and Quick Reference Guide for Evidence-Informed Curricula Selection
Bibliographic record
Abstract
This research project was conducted in collaboration with Heather Austin, OTR/L and the Puyallup School District. Through discussion with Heather, we determined that there is a need to study the efficacy of social-emotional learning (SEL) curricula for children with severe disabilities who often do not receive the same comprehensive SEL instruction as their peers in general education classrooms. A mixed-methods systematic review of the literature was conducted on strategies and interventions for SEL for students ages 3-12 years old in classrooms that serve students with severe disabilities. We analyzed 19 articles published in peer-reviewed journals by reviewing each for statistically significant results pertaining to SEL outcomes for the population of interest. Results indicated the majority of curricula included in this research had mixed to positive outcomes. Interventions with statistically significant findings included play-based treatments, art therapy, mindfulness, and theory of mind training, as well as branded curricula and strategies such as ICME, PATHS, Integra Social Competence Program, and Second Step.\nCritically Appraised Topic findings were translated into the development of a quick reference guide which was organized by SEL outcome and structured according to the Collaborative for Academic, Social, and Emotional Learning core competencies. Each outcome contained suggestions on dosage, intervention approaches, and resources for application. An in-service and survey were completed to evaluate usability of this product by practitioners and educators working in school-based settings. Fifty percent of respondents worked with students in self-contained classrooms. Overall the survey data revealed a positive trend of ratings and qualitative feedback from respondents and a good match of SEL outcomes addressed in the quick reference guide to needs identified by practitioners. A primary implication of our research is that the field of occupational therapy needs to capitalize on its potential to provide support to students with disabilities around their social participation and emotional regulation. Occupational therapy can support teachers in implementing SEL curricula and interventions to promote positive outcomes and reduce maladaptive behaviors.
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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 | 0.035 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.044 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".