A person-centered examination of adverse childhood experiences and associated distal health, mental health, and behavioral outcomes in the United Arab Emirates
Bibliographic record
Abstract
BACKGROUND: An increasing body of evidence highlights the utility of examining adverse childhood experiences (ACEs) utilizing person-centered analytical approaches, particularly for understanding the organization and co-occurrence of ACEs, and their contributions to risk, vulnerability, and the development of intervention efforts. METHODS: In the first study of its kind, this paper uses Latent Class Analysis, to assess ACEs among a large community sample in Abu Dhabi, capital city of the United Arab Emirates, by examining patterns of ACEs and their associated impact on health, mental health, behavioral risk, and adult psychological function in a cross-sectional sample of 922 members of the Abu Dhabi community. RESULTS: Findings support a 3-class solution, representing low-to-no ACEs, Household ACEs, and Violence ACEs among this sample, with variability in the age, sex, and nationality status reflected across classes. ACE categories notably differentiated later adult risk for a suite of diagnoses of health and mental health disorders, risk for elevated screening values for depression, anxiety and stress, and a range of adult risk-related behaviors. CONCLUSION: These findings are considered in line with the extant literature and form the basis of considerable public health policy and intervention planning in Abu Dhabi, the United Arab Emirates, and the Arab region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".