Prevalence and Correlates of Prenatal Depression, Anxiety and Suicidal Behaviours in the Volta Region of Ghana
Bibliographic record
Abstract
Pregnancy is associated with several physical and psychosocial challenges that influence women’s health and wellbeing. However, prenatal mental health has received little attention. Therefore, this study examined the prevalence and correlates of prenatal depression, anxiety and current suicidal behaviors among pregnant women in the Volta Region of Ghana. Two hundred and fourteen (n = 214) pregnant women recruited from two hospitals responded to the hospital depression and anxiety scale (HADS), the insomnia severity index, and a set of psycho-behavioral, socioenvironmental and demographic characteristic questions. Chi-squared, bivariate and multivariate logistic regression were used for data analysis. Prevalence of prenatal depression, anxiety and current suicidal behaviors was 50.5%, 35.5% and 3.3%, respectively. After controlling for other variables, average monthly income, insomnia, non-nutritious food consumption (pica), and body image satisfaction were significantly associated with depression. Marital status, insomnia, lifetime suicidal behavior and partner support were significantly associated with anxiety. Current partner abuse was the only factor significantly associated with current suicidal behavior. The high prevalence rates of anxiety and depression among pregnant women and intimate partner violence remain important maternal health issues in the region. Therefore, brief mental health screening and counseling services should be integrated into prenatal healthcare services.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".