Child maltreatment and neglect in the United Arab Emirates and relationship with low self-esteem and symptoms of depression
Bibliographic record
Abstract
OBJECTIVES: To our knowledge, this study is the first in the United Arab Emirates (UAE) to investigate the prevalence of child maltreatment in relation to depressive symptoms and self-esteem. STUDY DESIGN: Exposure to physical maltreatment, emotional abuse and neglect was evaluated in 518 adolescents (86% response rate) randomly selected from schools in Al Ain in the Emirate of Abu Dhabi. The Rosenberg self-esteem scale and the Beck Depression Inventory were used to measure self-esteem and depressive symptoms by using multivariate logistic regression analyses. RESULTS: The mean age of study participants was 14.3 years. Emotional abuse was the most frequent form of maltreatment (33.9%), physical abuse (12.6%) and neglect (12.1%) followed. Male sex was a positive predictor of physical abuse (OR = 2.12; 95% CI 1.18-3.77), whilst higher maternal level of education was protective (OR = 0.40; 95% CI 0.19-0.86). Daily screen time (OR = 2.77; 95% CI 1.17-6.56) and tobacco smoking (OR = 1.86; 95% CI 1.09-3.18) positively predicted emotional abuse. Emotionally maltreated and neglected participants were less likely to report high level of self-esteem and more likely to report symptoms of depression. CONCLUSIONS: Child maltreatment in the UAE is of a similar magnitude to what reported in other countries around the world and significantly associated with low self-esteem and depressive symptoms.
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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".