Prevalence of cognitive dysfunction and associated behavioral changes, lactational failure, and their determinants among postpartum women in South India: A community‐based study
Bibliographic record
Abstract
OBJECTIVES: To estimate the prevalence of cognitive dysfunction and associated behavioral changes and their prevalence among postpartum women, and also the prevalence of lactational failure and its determinants among postpartum women. METHODS: A cross-sectional study was conducted involving 200 postpartum women (65% rural, 35% urban). Data were collected through structured interviews and assessments using the Indian version of the Montreal Cognitive Assessment and Depression, Anxiety, and Stress Scale-21. Statistical analyses included chi-square tests and regression models. RESULTS: Cognitive dysfunction was observed in 25% of participants (21.5% mild, 3.5% moderate). Stress, anxiety, and depression were prevalent in 80.5%, 27%, and 30% of women, respectively. Lactational failure was reported by 21%, strongly correlated with cognitive dysfunction (P = 0.01) and mental health issues (stress, anxiety, and depression; P < 0.001). Key determinants of lactational failure included poor mother-partner relationships, lack of social support, low education levels, delayed breastfeeding initiation, childcare stress, comorbidities, mode of delivery, and low birth weight. Cognitive dysfunction was significantly associated with male offspring, insufficient milk production, lack of social support, and poor education levels (P < 0.001). CONCLUSION: The study highlights a strong association between maternal mental health, cognitive dysfunction, and lactational failure. Addressing psychosocial and demographic determinants through targeted interventions is critical for improving maternal and child health outcomes in postpartum populations.
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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 | 0.001 | 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".