Training and coaching early childhood teachers to foster social, emotional, and behavioral competence of children in Turkey.
Bibliographic record
Abstract
A growing evidence base demonstrates the effectiveness of teacher training and coaching interventions to improve teacher- and child-level outcomes in high-income countries. However, more information is needed to show the benefits of these interventions in low- and middle-income countries (LMICs). To provide an evidence base for LMICs, we conducted a cluster-randomized controlled trial examining the efficacy of a teacher training and coaching intervention for promoting children's social, emotional, and behavioral competence, Reaching Educators and Children (REACH) Classroom Check-Up (CCU), on teachers' behaviors, teacher-child relationship quality, and children's social competence and problem behaviors. Participants included 20 early childhood teachers and 175 children (4-6 year olds) in Turkey. Findings indicate that REACH CCU increased teachers' positive behaviors and teacher behaviors that support social, emotional, and behavioral competence of children, while reducing teachers' negative behaviors. Teachers in REACH CCU demonstrated an increased level of closeness and reduced levels of conflict with children in their classrooms. Furthermore, REACH CCU improved teacher-reported social competence and reduced problem behaviors of children. Results provide evidence that REACH CCU is a promising approach for improving teachers' behaviors, teacher-child relationship quality, and children's social, emotional, and behavioral competence, especially in LMICs. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".