Adverse Childhood Experiences and Changing Levels of Psychosocial Distress Scores across Pregnancy in Kenyan Women
Bibliographic record
Abstract
Background: Adverse childhood experiences (ACEs) have been associated with deleterious effects on mental health in pregnancy. Methods: The ACE International Questionnaire (ACE-IQ) was used to measure neglect, abuse, and household dysfunction. Longitudinal mixed effect modelling was used to test the effect of ACEs on pregnancy-related anxiety, depressive symptoms, and perceived stress at two time points (12–19 and 22–29 weeks) during pregnancy. Results: A total of 215 women who were predominantly married (81%) and had attained tertiary education (96%) were enrolled. Total ACEs were significantly associated with depressive symptoms (r = 0.23, p < 0.05) and perceived stress (r = 0.18, p < 0.05). As depressive symptoms decreased, t (167) = −8.44, p < 0.001, perceived stress increased, t (167) = 4.60, p < 0.001, and pregnancy-related anxiety remained unchanged as pregnancy progressed. Contact sexual abuse (p < 0.01) and parental death or divorce (p = 0.01) were significantly associated with depression over time (p < 0.01). Total ACEs in this study were associated with depressive symptoms early but not late in pregnancy. Conclusions: Higher total ACEs were positively associated with depressive symptoms and perceived stress during pregnancy, suggesting that mental disorders may have an impact on pregnancy outcomes and ought to be addressed. Further validation of the Edinburgh Postnatal Depression Scale (EPDS) tool in local settings is required.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".