Emotion Coaching Practice With the Whole School Approach “Emotionally Friendly Kindergarten Transformation Program” Process Analysis
Bibliographic record
Abstract
The preschool period must be the starting point for the same attitude among all education stakeholders to achieve sustainable improvement and permanent change. Undoubtedly, a supportive classroom environment with a bond and an emotion-oriented approach contributes to children’s intellectual and personal development. Emotion coaching is a relational behavior management approach that enables people/children to realize the emotions they are experiencing. The whole school approach facilitates the development of a common language between stakeholders. There is a need for studies that address emotion coaching and the whole approach together in early childhood. This study examined stakeholders’ perspectives on implementing the Emotion-Friendly Kindergarten Transformation Program (EFKTP), an emotion coaching program based on a whole school approach. Based on the case study method, the study consists of 34 individuals, including all stakeholders. The data were collected through focus group interviews and semistructured interviews with stakeholders regarding the eight stages of the EFKTP program. Descriptive and content analysis methods were used to evaluate the program’s overall impact. The results suggest that school applications of the program enhance children’s emotional repertoire and behavioral control, home activities enhance communication and relationships between children and their families, and emotion coaching training to stakeholders improves self-efficacy perceptions among teachers and parents.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".