The Degree of Contribution of Patterns of Mothers’ Treatment to the Emotional Balance of Kindergarten Children
Bibliographic record
Abstract
This study aimed to identify the degree of contribution of mothers’ treatment patterns to the emotional balance of kindergarten children and to achieve the objectives of the study; two scales were built: a questionnaire to measure mothers’ treatment patterns and a questionnaire to measure the emotional balance of kindergarten children after ensuring their validity and reliability. The study sample consisted of 195 children aged 5-6 who were selected from the private kindergartens of the Naour Brigade in Amman Governorate in Jordan. After using arithmetic means, standard deviations, Pearson correlation coefficient, and multiple regression analysis, the results of the study reached the following: The most common treatment pattern for mothers was the democratic one. The level of emotional balance among kindergarten children was average. The results showed a positive, statistically significant correlation at the significance level (α = 0.05) between the total score of the emotional balance scale for kindergarten children and the democratic treatment pattern of mothers. The results revealed a statistically significant negative correlation at the significance level (α = 0.05) between the total score of the emotional balance scale for kindergarten children and the two types of mothers’ authoritarian and abusive treatment. The regression analysis results revealed that the patterns of mothers’ treatment contribute to the emotional balance of kindergarten children by 34.2%. The patterns of mothers’ treatment contributed in varying proportions to the emotional balance of kindergarten children, the highest of which was the authoritarian pattern, then the neglectful pattern, and finally the democratic pattern.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".