Impact of a Sports Psychology–Based Physical Activity Intervention on Emotional Regulation in Early Childhood Education
Bibliographic record
Abstract
The study employed The Post-test experimental control-group. Quasi- experimental research design was used in this study where the experimental groups were exposed to Instructor-Led Intervention Blending Play Based Sport Drills and Emotion-Coaching Therapy (ILIBPESD/ECT) were used as intervention while control group was exposed to usual conventional class of poem citation.Pupils in Early Childhood Care centres formed population of the study as simple random sampling technique was adopted in selection of 20 pupils (5-6years) each from 4 ECE centres in Ikeja LGA, of Lagos State to form 80 preschoolers. A researcher designed checklist that contains 20-items on 3 response format of available, partially available and not available was used in data collection. The Emotional Regulation Checklist was also used to observe the sampled population. Data was analyzed using t-test and Analysis of Covariance (ANCOVA). All the hypotheses were tested at .05 level of significance. Results revealed statistically significant improvements in emotional-regulation scores for the intervention group with marginal gain in favourable emotional expression and peer conflict resolution. It then concluded that embedding sports-psychology-informed physical activities within early childhood curriculum enhances a better emotional regulation and management mechanism as it recommends that scaling such integrative curricula with teacher training and ongoing fidelity monitoring would go a long way to have stable mental ready learners in classrooms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".