Prevalence and risk factors for postpartum depression among women with preterm and low‐birth‐weight infants: a systematic review
Bibliographic record
Abstract
BACKGROUND: Although much is known about the risk factors for postpartum depression (PPD), the role of giving birth to a preterm or low-birth-weight infant has not been reviewed systematically. OBJECTIVE: To review systematically the prevalence and risk factors for PPD among women with preterm infants. SEARCH STRATEGY: Medline, CINAHL, EMBASE, PsycINFO and the Cochrane Library were searched from their start dates to August 2008 using keywords relevant to depression and prematurity. SELECTION CRITERIA: Peer-reviewed articles were eligible for inclusion if a standardised assessment of depression was administered between delivery and 52 weeks postpartum to mothers of preterm infants. DATA COLLECTION AND ANALYSIS: Data on either the prevalence of PPD or mean depression score in the target population and available comparison groups were extracted from the 26 articles included in the review. Risk factors for PPD were also extracted where reported. MAIN RESULTS: The rates of PPD were as high as 40% in the early postpartum period among women with premature infants. Sustained depression was associated with earlier gestational age, lower birth weight, ongoing infant illness/disability and perceived lack of social support. The main limitation was that most studies failed to consider depression in pregnancy as a confounding variable. AUTHOR'S CONCLUSIONS: Mothers of preterm infants are at higher risk of depression than mothers of term infants in the immediate postpartum period, with continued risk throughout the first postpartum year for mothers of very-low-birth-weight infants. Targeted clinical interventions to identify and prevent PPD in this vulnerable obstetric population are warranted.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".