Exploring adverse childhood experiences (ACEs) among Ugandan university students: its associations with academic performance, depression, and suicidal ideations
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs) among university students have been linked to a variety of factors and have been shown to have a dose-response relationship with adult health and behavior. OBJECTIVE: To investigate the effect of exposure to ACEs on academic performance, depression, and suicidal ideations among university students. METHODS: A cross-sectional survey among university students at a public university in southwestern Uganda was conducted in 2021, integrating the Adverse Childhood Experiences International Questionnaire for assessing ACEs, the Patient Health Questionnaire for assessing depression symptoms and suicidal ideations, and questions assessing the family structure and academic performance as adopted from similar studies. Regression analysis was performed, and 3 models were generated to answer the study hypotheses. RESULTS: A total of 653 undergraduate university students with a mean age of 22.80 (± 3.16) years were recruited. Almost all students (99.8%) experienced one or more ACEs, with physical abuse being the common ACE reported. The average depression symptom severity was statistically higher among individuals who experienced any form of ACEs. No relationship was observed between the ACEs experienced and self-rated academic performance. Similarly, on regression analysis, the cumulative number of ACEs was not associated with self-rated academic performance (β = - 0.007; 95% CI - 0.031 to 0.016; p = 0.558). However, the cumulative number of ACEs was positively associated with depression symptom severity (β = 0.684; 95% CI 0.531-0.837; p < 0.001), as well as increased the likelihood of suicidal ideations (aOR = 1.264; 95% CI 01.090-1.465; p < 0.001). CONCLUSIONS: The burden of ACEs is exceedingly high among Ugandan university students, highlighting the urgency in strengthening effective child protection strategies to protect Uganda's rapidly growing population from mental ill-health and avoid future psychological disability, a burden to the healthcare system. The study's findings will also be useful to practitioners/policymakers working to prevent/limit child maltreatment globally.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".