Prevalence and risk factors of postpartum depression among women living in the United Arab Emirates
Bibliographic record
Abstract
PURPOSE: Postpartum depression received almost no attention in the United Arab Emirates (UAE). The aim was to examine the prevalence of depressive symptomatology and the associated risk factors among women in the UAE. METHODS: A prospective cohort study recruited women from postpartum wards in hospitals across four emirates in the UAE. Women completed questionnaires immediately after childbirth and at 3 and 6 months postpartum. Depressive symptomatology was measured using the Edinburgh Postnatal Depression Scale (EPDS > 12). Risk factors were identified using the generalized estimating equation. A stratified analysis of the postpartum period was performed. RESULTS: Among the 457 women recruited, 35% exhibited depressive symptomatology within the first 6 months postpartum. Younger women (< 25 years), part-time employment, the receipt of financial support from the family, and difficulty in managing monthly income were associated with a higher risk of postpartum depression. Husband's employment, husband's support, and living in own house were associated with a lower risk of postpartum depression. Maternity leave of more than 3 months increased the risk of depression during the first 3 months postpartum. From 3 to 6 months postpartum, Muslim women had a higher risk of depression whereas women who breastfed other children and in the past 7 days, and perceived their infant as healthy had a lower risk of depression. CONCLUSIONS: The prevalence of maternal depressive symptomatology is considerable in the UAE. Risk factors change over the 6-month postpartum period suggesting the need for an innovative multidisciplinary approach to the management of postpartum depression, including follow-up screening.
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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".