Investigating The Effect of Excellent Parenting Program on The Behavioral Problems of Hearing Impaired Children
Bibliographic record
Abstract
Objective: This research was aimed at the effect of excellent parenting program on the behavioral problems of hearing impaired children. Materials and Methods: The present research method was quasi-experimental with a pre-test, post-test and follow-up design. The statistical population includes all parents of 4-6-year-old hearing-impaired children in Tehran in 2022, which was selected by simple random method among deaf and hard-of-hearing centers and associations. The number of samples in this study was 30 parents who were randomly assigned to two groups of 15 people, test and control. The experimental group was trained in 13 sessions of 90 minutes, 3 sessions a week; while for the control group, no special action was taken. After the training course, the parents of both groups were subjected to a post-test. The follow-up phase was also implemented after 45 days. The tools used were the child's abilities and behavioral problems questionnaire (Goodman, 1997). Findings: Data analysis was carried out using analysis of variance with repeated measurements and the results showed that excellent parenting program had a significant effect on the behavioral problems of hearing impaired children (p<0.05). Conclusion: Based on the findings, it can be concluded that excellent parenting program leads to reduction of behavioral problems of hearing impaired children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".