Postpartum Mental Health and Its Relationship with Mediating Social Determinants of Health in Iran based on the WHO Model: A Systematic Review
Bibliographic record
Abstract
Background: Pregnancy is a complex and vulnerable period that causes some challenges including the development of postpartum psychiatric disorders (PPDs) for women. Identifying the factors associated with these disorders can be effective in reducing maternal symptoms and supporting mother, child and family. The aim of this study was to identify the relationship between postpartum mental health and mediating social determinants of health in Iran.Methods: In this systematic review, the Persian and English observational studies in Iran were obtained through advanced search in online databases, such as PubMed, Scopus, EMBASE, SID, Magiran, Psycinfo, and Google Scholar search engine in the period of January 2005 to August 5, 2021 using the following keywords: social determinants, mediating factors, social support, mental health, risk factors, postpartum, Iran, and their English equivalents through Mesh. Articles were selected based on the inclusion and exclusion criteria and quality assessment of articles was performed using the standard Newcastle-Ottawa Scale (NOS).Results: Out of 42 eligible articles (total sample: 39216), 40 articles examined the relationship between postpartum depression and 2 articles studied the relationship of maternal grief with some mediating social determinants. In general, these factors were classified into five categories, including midwifery and pregnancy-related factors, psychosocial conditions, factors related to postpartum status, behavioral factors and material status or conditions, and health care.Conclusion: Mothers' mental health is affected by many underlying factors; Therefore, identifying the risk factors associated with mental disorders in this population based on the model of the WHO (World Health Organization), especially in the mediating area (Material and environmental conditions, Psychosocial, Behavioral factors, Health system) due to the extent of this area is very important.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".