Mothers’ Attitudes and Self-Esteem among Deaf Children in Iranian High Schools
Bibliographic record
Abstract
This study examines the relationship between mothers’ attitude and self-esteem among deaf children who are currently enrolled in deaf high schools in Iran. While family system theory is typically used in trying counseling and therapy, much can be learned from examining it by studying the relationship between deaf children and their mothers. Family systems theory has been used in trying to understand problems of communication between children and their families (Widerman, 1995). In general, attention with some significant findings has been focused on environmental factors such as parenting, communication at home, and type of schooling as potential contributors to self-esteem (Crocker, 2008). However, the role of other variables such as family’s attitude has not been systematically explored. This implies that the picture is far from complete. It is also not clear what kind of factors effect on deaf children’s self-esteem. Henceforth, the findings of this study may be helpful in the development of curriculum goals for increasing self-esteem in deaf children. Counselors and social workers can do much to promote positive family relationships as well as acceptance of disabilities in deaf children. The sample consisted of 200 deaf children (100 boys and 100 girls) and 200 normal-hearing mothers. The study was conducted at deaf high schools in Iran. Rosenberg Self-Esteem scale, Parental Attitudes towards Deafness and interviews Scale were used. The results showed that a relationship existed between mothers’ attitude and deaf children’s self-esteem. Additionally, according to the Pearson correlation test, there is a relationship between the mothers’ attitudes and self-esteem in deaf children. Children whose mothers ably communicated had higher self-esteem scores than their counterparts whose mothers could not ably communicate. The findings of this study may be helpful in the development of curriculum goals for increase of self-esteem in deaf children. Counselors can do much to promote positive family relationships as well as acceptance of disabilities in deaf children. Also, these findings may expand our understanding of the characteristics of deaf children’s self-esteem and their families’ attitudes. In terms of practical value, it is hoped that these findings provide information that may help determine efficacy of self-esteem, for deaf children and their families.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".