Life events, social support and depression in childbirth: perspectives from a rural community in the developing world
Bibliographic record
Abstract
BACKGROUND: High rates of depression associated with childbirth have been reported in many parts of the developing world. However, the prevalence and associations of antenatal and post-natal depression in the rural population remain unknown. Disability associated with depression and its impact on infant health and development could have important public health implications for many developing countries where large proportions of the population are rural. METHOD: All women living in southern Kahuta, Pakistan, in their third trimester of pregnancy were interviewed at 6 weeks before delivery (N = 632) and again at 10-12 weeks after delivery (N = 541), using WHO Schedule for Clinical Assessment in Neuropsychiatry (SCAN), Personal Information Questionnaire (PIQ) and Brief Disability Questionnaire (BDQ). RESULTS: The point prevalence of ICD-10 depressive disorder was 25% in the antenatal period and 28 % in the post-natal period. Depressed mothers were significantly more disabled, had more threatening life events, and poorer social and family support than non-depressed mothers. Vulnerable mothers were more likely to be depressed during pregnancy, rather than have an onset in the post-natal period. CONCLUSION: Over one-quarter of mothers in a rural sub-district of Pakistan suffer from depression shortly before and after childbirth. Rapidly changing traditional family structures and practices may be increasing the risk of depression in many women. Recognizing and treating depression should be initiated during the antenatal, rather than post-natal period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".