SEL’s Impact on Reducing Psychological Barriers for Low-SES Students: Review and Future Directions
Bibliographic record
Abstract
Against the backdrop of growing recognition of SEL's potential to promote holistic student development, this study systematically examines the extent to which existing literature, sourced through the EBSCO database and rigorously screened using Covidence, addresses the impact of social-emotional learning (SEL) on reducing psychological barriers among primary school students with a low socioeconomic background. The literature analysis, which covers a broad range of studies, reveals several key findings: limited exploration of the underlying psychological mechanisms, an overreliance on quantitative methods that may overlook nuanced qualitative insights, neglect of individual-level effects that can vary significantly across diverse student populations, and insufficient attention to cultural identity awareness, which is crucial for fostering a sense of belonging and resilience in low-SES students. Our study not only highlights these critical gaps in the current research but also suggests promising avenues for further investigation, aiming to better understand and enhance the effectiveness of SEL interventions in addressing psychological barriers among students from low socioeconomic status backgrounds. By doing so, we hope to contribute to the ongoing dialogue and practical implementation of SEL programs in educational settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".