Is it clinically and cost effective to screen for postnatal depression: a systematic review of controlled clinical trials and economic evidence
Bibliographic record
Abstract
BACKGROUND: Postnatal depression (PND) is a common mental health problem, which is associated with adverse consequences beyond the individual with depression. It is not known whether using formal methods to identify PND are clinically and cost effective in improving maternal and infant outcomes. OBJECTIVES: To evaluate the clinical and cost effectiveness of antenatal and postnatal identification of depressive symptoms. SEARCH STRATEGY: Twenty electronic databases were searched to retrieve English and non-English language articles published until February 2007. SELECTION CRITERIA: Randomised controlled trials or controlled trials comparing the use of formal methods to identify PND, with or without enhancement of care, or feedback of scores with not using formal methods to identify PND or usual care. DATA COLLECTION AND ANALYSIS: Two reviewers independently assessed studies for inclusion and extracted data. Results from the trials were combined to calculate odds ratios and 95% confidence intervals for dichotomous outcomes. MAIN RESULTS: Five studies were identified that compared formal use of a method to identify PND, with or without enhancement of care, or feedback of scores with not using a formal method or usual care. All of the studies used the Edinburgh Postnatal Depression Scale (EPDS) to identify women with PND. The results of the studies indicated beneficial effects of using the EPDS in reducing EPDS scores (OR = 0.61; 95% CI 0.48-0.76). AUTHOR'S CONCLUSIONS: Despite some apparent beneficial effects of using formal methods to identify PND, it is difficult to disentangle the effects of the screening component alone from interventions linked to a positive screen as some of the studies included enhancements of care and/or an intervention.
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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 | 0.038 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.016 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".