Depression screening and patient outcomes in pregnancy or postpartum: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Clinical practice guidelines disagree on whether health care professionals should screen women for depression during pregnancy or postpartum. The objective of this systematic review was to determine whether depression screening improves depression outcomes among women during pregnancy or the postpartum period. METHODS: Searches included the CINAHL, EMBASE, ISI, MEDLINE, and PsycINFO databases through April 1, 2013; manual journal searches; reference list reviews; citation tracking of included articles; and trial registry reviews. RCTs in any language that compared depression outcomes between women during pregnancy or postpartum randomized to undergo depression screening versus women not screened were eligible. RESULTS: There were 9,242 unique titles/abstracts and 15 full-text articles reviewed. Only 1 RCT of screening postpartum was included, but none during pregnancy. The eligible postpartum study evaluated screening in mothers in Hong Kong with 2-month-old babies (N=462) and reported a standardized mean difference for symptoms of depression at 6 months postpartum of 0.34 (95% confidence interval=0.15 to 0.52, P<0.001). Standardized mean difference per 44 additional women treated in the intervention trial arm compared to the non-screening arm was approximately 1.8. Risk of bias was high, however, because the status of outcome measures was changed post-hoc and because the reported effect size per woman treated was 6-7 times the effect sizes reported in comparable depression care interventions. CONCLUSION: There is currently no evidence from any well-designed and conducted RCT that screening for depression would benefit women in pregnancy or postpartum. Existing guidelines that recommend depression screening during pregnancy or postpartum should be re-considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".