Adverse childhood experiences and common mental disorders among young people in Ethiopia: a cross-sectional study using WHO ACE-IQ
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore adverse childhood experiences (ACEs) and their association with common mental disorders (CMD) among college students in Ethiopia. DESIGN: Cross-sectional study. SETTING: Addis Ababa University, College of Health Sciences. PARTICIPANTS: A total of 345 participants completed the whole questionnaire. METHODS AND MATERIALS: The study used a stratified random sampling technique. Data were gathered through self-administered questionnaires. The instruments used included adapted sociodemographic questions, the ACEs International Questionnaire, the Patient Health Questionnaire, the Generalized Anxiety Disorder Scale and a brief tool for assessing substance use. To examine the relationship between ACEs and various independent variables, both binary and multivariate logistic regression analyses were employed. RESULT: In the total sample (n=345), the participant's mean age was 22.2 (± 2.03), with the majority being females (58%). About 16% of the participants reported depression symptoms and 14.2% had anxiety. The majority of the participants (80%) had at least one ACE and one quarter (25.2%) of the participants had experienced four or more ACEs. The most prevalent type of ACE was community violence (35.4%). One fifth (20%) of the participants had reported having experienced childhood sexual abuse. After controlling for confounding variables, those with four or more ACEs were 6.17 times (adjusted OR (aOR) 6.17; 2.51, 15.18) and 6.0 times (aOR 6.0; 2.25, 16.02) more likely to have depression and anxiety, respectively. CONCLUSION: There was a dose-response relationship between ACEs and both anxiety and depression. Identifying and preventing ACEs at an early stage could contribute to reduce depression and anxiety among young people. Efforts to prevent ACEs should target not only individuals but also extend to households and communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".