Emotional regulation in Preschoolers with Autism Spectrum Disorder: A PRISMA Style Review
Bibliographic record
Abstract
Objective: Emotional regulation (ER) is a critical developmental skill often disrupted in autistic preschool-aged children, negatively impacting their adaptive functioning, parental well-being, and broader developmental outcomes. This scoping review aims to synthesize existing literature on ER in autistic preschoolers, providing an overview of current research, identifying intervention strategies, and highlighting methodological gaps to inform future research. Methods: A systematic literature search was conducted using PRISMA guidelines, identifying 17 studies that investigated ER in autistic preschoolers. Inclusion criteria focused on studies examining ER in preschool-aged children (ages 2-6) diagnosed with autism, while exclusion criteria filtered out studies lacking direct relevance to ER or those focused on older populations. Results: Results indicated that autistic preschoolers demonstrated heightened emotional reactivity, prolonged emotional recovery times, and a reliance on maladaptive or caregiver-supported ER strategies. Intervention strategies, such as parent-mediated programs and cognitive-behavioral approaches, showed measurable changes in improving ER abilities and enhancing caregiver confidence in this population. However, the review also highlighted significant gaps in literature, including inconsistent use of validated tools across studies, limited representation of culturally and socioeconomically diverse populations, and a lack of longitudinal data. Conclusion: The findings of this scoping review hope to underscore the importance of ER in autistic preschoolers and the need for culturally inclusive research and standardized methodologies to guide effective interventions and support improved developmental outcomes.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".