Prevalence and Factors Associated With Depression Among Antenatal and Postnatal Women Attending Government Health Clinics in Selangor, Malaysia: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Maternal mental health concerns either during pregnancy or the postpartum period are a public health challenge. Depression, anxiety, and stress can lead to poorer outcomes in the antenatal and postpartum periods. There is evidence that the effect of stress, anxiety, and depression during pregnancy negatively affects fetal neurodevelopment and children's developmental outcomes. Early diagnosis can improve treatment outcomes and prevent negative impacts on the mother and baby. Identifying risk factors, such as age, socioeconomic status, mental health history, and family dysfunction, and clinical manifestations are important for public health programs. OBJECTIVE: This study aims to determine the prevalence of depression and associated risk factors among antenatal and postnatal women attending government health clinics in Selangor. METHODS: A multicenter cross-sectional study will be conducted among antenatal and postnatal women attending government health clinics (Klinik Kesihatan) in Selangor, Malaysia, from August 1, 2024, to December 31, 2026. We will perform a simple random sampling in all 9 districts in Selangor to select 1 government health clinic in each district. The inclusion criteria for this study are women aged 18 years and older who are either pregnant or delivered a newborn or stillborn child within the preceding 6 weeks. We will use a published screening tool (Edinburgh Postnatal Depression Scale) to determine the level of depression among antenatal and postnatal women. We will also collect data on sociodemographic characteristics, obstetric factors, and psychosocial support. RESULTS: We will obtain ethics approval from the relevant ethics boards prior to data collection. Data will be analyzed using SPSS version 26.0 (IBM Corporation) and we will conduct a descriptive analysis to determine the prevalence of depression. We will calculate the level of depression among antenatal and postnatal women and score it based on a previous study conducted in Malaysia, with a score of ≥12 indicating the presence of depression. The association between depression and risk factors will be determined by multiple logistic regression analysis. P values less than .05 will be considered statistically significant. CONCLUSIONS: Depression is one of the mental health complications that may arise following childbirth. Therefore, the findings of this study on the prevalence and associated risk factors of depression among antenatal and postnatal women in Selangor may help women address this challenge and improve maternal mental health during pregnancy and after birth. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63663.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".