Prevalence and risk factors of postpartum depression in Saudi Arabia: systematic review
Bibliographic record
Abstract
Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, with varying prevalence rates influenced by sociocultural and economic factors. In Saudi Arabia, studies report widely divergent PPD rates, necessitating a systematic synthesis of existing evidence. This systematic review aimed to determine the prevalence of PPD in Saudi Arabia and identify key risk and protective factors. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and ScienceDirect was conducted. Twenty-two observational studies (cross-sectional, case-control, and retrospective) assessing PPD prevalence and associated factors in Saudi mothers within 1 year postpartum were included. Data were extracted on study characteristics, PPD prevalence (assessed via Edinburgh Postnatal Depression Scale (EPDS) or clinical diagnosis), and risk/protective factors. Risk of bias was evaluated using the Newcastle-Ottawa Scale. PPD prevalence ranged from 5.1% to 75.7%, with heterogeneity attributed to regional and methodological differences. Key risk factors included lack of social/spousal support (reported in 10 studies), history of depression (8 studies), cesarean delivery (6 studies), and financial stress. Breastfeeding and higher income emerged as protective factors. Most studies (15/22) had a moderate risk of bias due to convenience sampling and unadjusted confounders. PPD is highly prevalent in Saudi Arabia, with risk factors rooted in biological, psychological, and sociocultural contexts. Findings underscore the need for targeted interventions, including enhanced social support systems, routine screening, and culturally sensitive mental health policies.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".