Economic burden of postpartum depression among mothers: Systematic review
Bibliographic record
Abstract
Postpartum depression (PPD) is a common problem among nursing mothers. PPD has been associated with several adverse effects on mothers and households around the world. This aimed to investigate the economic burden of PPD on mothers' households. This review used the PRISMA-SR guideline for systematic reviews. The researchers conducted extensive searches on Google Scholar, Science Direct, and PubMed using the search term developed for this study. A total of 235 results were retrieved from the three online databases. These results were screened based on the inclusion and exclusion criteria formulated. After a thorough screening, 13 articles were included in the review. This review shows that economic uncertainty contributes to PPD among mothers worldwide. Some of these economic uncertainties identified in the literature include the social and economic cost of having a child, high inflation, economic deprivation, poor relationships, income dissatisfaction, and financial stress and insecurity. In addition, this study revealed that mothers with PPD place a high financial burden on their households. Based on country, Canada has the highest cost, followed by South Africa, the USA, and Great Britain. Research on the economic burden of PPD among mothers is still developing globally due to limited literature on the topic. This review concludes that the economic burden emanating from mothers' environments engenders PPD, which also imposes an economic burden on households. This study recommends global subsidisation of medical care costs for mothers with PPD by the WHO to achieve SDG goals 1 (addressing the disproportionate impact of poverty on women) and goal 3 (increasing health financing and workforce recruitment in 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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".