A controlled study of postpartum depression among Nepalese women: validation of the Edinburgh Postpartum Depression Scale in Kathmandu
Bibliographic record
Abstract
OBJECTIVES: To measure the prevalence of depression amongst postpartum and non-postpartum Nepalese women in Kathmandu using the Edinburgh Postpartum Depression Scale (EPDS) and to assess the ease of use and validity of the scale compared with Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for major depression. METHODS: We screened 100 women 2-3 months post-delivery and 40 control women using the EPDS. All those who screened positive for depression and 20% of the negatives also underwent a structured interview to assess depression by DSM-IV criteria. RESULTS: Predictive errors were minimized by using an EPDS score > or =13 to define depression. Using this threshold, there was no difference in depression prevalence between postpartum women (12%) and the control group (12.5%) (Fisher's exact test, P > 0.05). Compared with DSM-IV, the sensitivity, specificity and positive predictive values were 100, 92.6 and 41.6%, respectively. CONCLUSIONS: The prevalence of postpartum depression (PPD) in Nepalese women and the validity and ease of use of the EPDS in the setting of a postnatal clinic in Kathmandu are all surprisingly similar to the results of numerous studies in developed countries. Despite poor living conditions, PPD is no more common than the background depression rate amongst Nepalese women. It can be reliably detected by trained clinical nurses using the EPDS screening test. These results may have implications for the planning of mental health resources for women in other developing countries.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".