Link between insomnia and perinatal depressive symptoms: A meta‐analysis
Bibliographic record
Abstract
Abstract Evidence shows the possible link between insomnia and perinatal depressive symptoms. In order to find a convergent quantitative answer, we collected data via the search of Medline, EMBASE and reference tracking, which included nine studies (a total sample of 1,922 women). An aggregate effect size estimate (correlation coefficient) was generated using the comprehensive meta‐analysis software. For the meta‐analytic procedure, a random effects model was set a priori. Moderating factors, including study design, method of assessment of depression, geographical origin of data, publication year, mean age, % married, breastfeeding rate, quality and type of data, % primiparous and history of depression, were examined via categorical or univariate mixed‐effects (method of moments) meta‐regression methods. Heterogeneity and publication bias were examined using standard meta‐analytic approaches. We found a significant, medium‐size relationship between insomnia and perinatal depressive symptoms (point estimate, 0.366; 95% confidence interval [CI], 0.205–0.508; p < 0.001; n = 9) and this was significantly heterogeneous (Q, 118.77; df, 8; p < 0.001; I2, 93.26%). The effect size estimate was significant for studies reporting no history of depression (point estimate, 0.364; 95% CI, 0.035–0.622; p < 0.05; n = 5) and for study design. With meta‐regression, no moderating factor (age, marriage rate, breastfeeding rate, pregnancy history or publication year) significantly mediated the effect size estimate. The depression assessment scale used, but not other categorical variables, explained the magnitude of heterogeneity. We found that insomnia during the perinatal period is associated with depressive symptoms, which warrants screening pregnant mothers for insomnia and depression.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".