Depression by Association? Mental Well-Being of Women in Urban Slums of Pakistan
Bibliographic record
Abstract
BACKGROUND: The association of mental health with parenthood is complex and varies across many social contexts. Previous studies place mothers with young children at a high-risk for depression. Therefore, the study aimed to understand the association of parity as a risk factor for maternal depression in a cross-sectional survey. METHOD: A total of 255 women were surveyed at two primary health care centers in Karachi, a Metropolitan city of Pakistan between May 2019 and July 2019 with an anonymously answered Public Health Questionnaire (PHQ) 9. The demographic characteristics and related variables were determined as potential correlates of vulnerability to maternal depression. Significant predictive factors associated with risk factors were analyzed by means of linear correlation and multiple regression analysis. RESULTS: The PHQ 9 score noted an 89.2% prevalence of depression in the study sample. Of those, 72.6% (0.001 p-value) were multiparous women (3-5 children). When analyzed within each individual parity category, grand-multiparous women (6 or more children) had the highest percentage of depression at 92.6% followed by multiparous women (2-5 children) at 90.6%. CONCLUSION: The result showed the greatest frequency of depression among multiparous women. However, grand multiparous unemployed women were at the highest risk of depression among low-income urban 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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".