Prevalence of Psychological Problems Amongst Iranian Immigrant Children and Adolescents In UK
Bibliographic record
Abstract
Objective: This study was designed to estimate prevalence rates of psychological problems in immigrant Iranian children in the UK and to evaluate the associated characteristics. Methods: A group of 244 children and adolescents, 111 boys and 133 girls between the ages 6 to 15 was selected. The children were categorised into groups with different psychological problems by their teachers on the Teacher’s Report Form (TRF). Also, the parents and The children completed the Child Behaviour Checklist (CBCL) and the Youth Self-Report (YSR) for the ages 11 to 15 years, respectively. Results: Two-way ANOVAs using gender and age groups as factors showed that there were significant effects of gender in these subscales. Attention problems (p<0.001), delinquent behaviour (p<0.001), aggressive behaviour (p<0.01), externalising (p<0.001) and total problems (p<0.02). Two-way ANOVAs using age-group and gender as factors showed that there were no significant effects of age in the eight subscales of the CBCL; although a trend toward significance was observed for the withdrawn subscale. The interactions between gender and age for all subscales were not significant. Conclusion: The results show that the level of psychological problems in this group is as high as their counterparts in Iran and Achenbach's US normative samples, if not higher. This might result from immigration stressors and the pressure of bilingual education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".