Appraisal of systematic reviews on interventions for postpartum depression: systematic review
Bibliographic record
Abstract
BACKGROUND: Postpartum depression (PPD) is a highly prevalent mental health problem that affects parental health with implications for child health in infancy, childhood, adolescence and beyond. The primary aim of this study was to critically appraise available systematic reviews describing interventions for PPD. The secondary aim was to evaluate the methodological quality of the included systematic reviews and their conclusions. METHODS: An electronic database search of MEDLINE, Embase, and the Cochrane Library from 2000 to 2020 was conducted to identify systematic reviews that examined an intervention for PPD. A Measurement Tool to Assess Systematic Reviews was utilized to independently score each included systematic review which was then critically appraised to better define the most effective therapeutic options for PPD. RESULTS: Of the 842 studies identified, 83 met the a priori criteria for inclusion. Based on the systematic reviews with the highest methodological quality, we found that use of antidepressants and telemedicine were the most effective treatments for PPD. Symptoms of PPD were also improved by traditional herbal medicine and aromatherapy. Current evidence for physical exercise and cognitive behavioural therapy in treating PPD remains equivocal. A significant, but weak relationship between AMSTAR score and journal impact factor was observed (p = 0.03, r = 0.24; 95% CI, 0.02 to 0.43) whilst no relationship was found between the number of total citations (p = 0.27, r = 0.12; 95% CI, - 0.09 to 0.34), or source of funding (p = 0.19). CONCLUSION: Overall the systematic reviews on interventions for PPD are of low-moderate quality and are not improving over time. Antidepressants and telemedicine were the most effective therapeutic interventions for PPD treatment.
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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 | 0.044 | 0.187 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".