Antenatal Depressive Symptoms in Rwanda: Rates, Predictors, and Social Support
Bibliographic record
Abstract
Abstract BackgroundPrevalence of perinatal depression is high in Rwanda and has been found to be associated with the quality of relationship with partners. This study extends this work to examine the relationship between antenatal depressive symptoms and social support across several relationships among women attending antenatal care services. Methods Structured survey interviews were conducted with 396 women attending antenatal care services in 4 health centers in the Southern Province of Rwanda. The Edinburgh Postnatal Depression Scale (EPDS) and Maternity Social Support Scale (MSSS) were used to assess antenatal depressive symptoms and the level of support respectively. Socio-demographic and gestational information, pregnancy intentions, perceived general health status, and experience of violence were also collected. Univariate, bivariate analyses and a multivariate logistic regression model were performed to determine the relationship between social support and predictors of antenatal depressive symptoms. ResultsMore than half of respondents were married (55.1%) or living with a partner in a common-law relationship (28.5%). About a third (35.9%) were in their 6th month of pregnancy; the rest were in their third term. The prevalence of antenatal depressive symptoms was 26.6% (EPDS ≥ 12). Bivariate analyses suggested that partner and peer support negatively predict depression level symptoms.Adjusting for confounding variables such as unwanted pregnancy (AOR: 0.415, CI: 0.221- 0.778), parity (AOR): 0.336, C.I: 0.113–1.000) and exposure to extremely stressful life events (Odds Ratio = 2.300, C.I. = 1.263–4.189), husband/partner support (AOR: 4.458, CI: 1.833- 10.842) was strongly significantly associated with antenatal depressive symptoms such that women reporting good support were less likely to report depression level symptoms than those reporting poor support or those with no partner. Peer support was no longer significant.Conclusion The study revealed that social support may be a strong protector against antenatal depressive symptoms but only support from the partner. This suggests that strengthening support to pregnant women may be a successful strategy for reducing the incidence or severity of maternal mental health problems, but more work is required to assess whether support from peers can compensate for absent or unsupportive partners.
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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 | 0.000 | 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".