Bibliographic record
Abstract
BACKGROUND: Supportive relationships during the perinatal period may enhance a mother's feeling of wellbeing and control. Support to women during labour and after birth has shown benefits and this may also be the case for mothers with postpartum depression. OBJECTIVES: The objective of this review was to assess the effect of professional and/or social support interventions for the treatment of postpartum depression. SEARCH STRATEGY: We searched the Cochrane Pregnancy and Childbirth Group trials register. SELECTION CRITERIA: Randomised and quasi-randomised trials comparing additional support from caregivers with usual forms of care in the postpartum period, in women who were clinically depressed in the six months after giving birth. DATA COLLECTION AND ANALYSIS: Trial quality was assessed and data were extracted by both reviewers. Study authors were contacted for additional information. MAIN RESULTS: Two studies involving 137 women were included. There is potential for bias in at least one study, due to large numbers of women refusing to take part in the trial as well as significant losses to follow-up during the trial. Treatment of postpartum depression with support was associated with a reduction in depression at 25 weeks after giving birth (odds ratio 0.34, 95% confidence intervals 0.17 to 0.69). REVIEWER'S CONCLUSIONS: There is some indication that professional and/or social support may help in the treatment of postpartum depression. The types of support should be investigated to assess which models are most effective.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".