Effectiveness of Home Visiting Programs To Prevent Maternal Depression: A Systematic Review of RCTs
Bibliographic record
Abstract
During prenatal and post-partum period, 10-20% of women experience depression. The quality of life and functional capacity of these women were also significantly impacted by depression. This condition also has an adverse effect on fetal development and newborn. Furthermore, home visit have been used to reduce maternal depression. However, the evidence of this still low. Hence, this review aims to evaluate the effectiveness of home visiting in preventing maternal depression. Articles for this systematic review were collected from several databases (PubMed, Scopus, Google Scholar, and ScienceDirect) using terms related to maternal depression, prevention, and home visiting. The quality of included studies were assessed using the Newcastle-Ottawa Scale (NOS). This systematic review is comprised of 13 high quality randomized clinical trials with 4.804 participants. Eleven studies indicate that home visiting effectively reduce depressive symptoms and a study shows that mothers receiving home visiting are twice less likely to develop depressive symptoms. Home visiting are also beneficial for low-income women amidst the low rate of mental health services. Only two studies state that there is no evidence that home visiting effectively reduces depressive symptoms and increases caregiving quality. However, some studies state that the mother's cognitive, child growth, and maternal depression may improve with home visiting integrated with cognitive behavioral therapy, counseling, education such as lectures, or video. Home visiting are effective in preventing maternal depression. Further studies are needed to compare the effectiveness of the intervention plan in home visiting.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".