Pregnancy anxiety and preterm birth: The moderating role of sleep.
Bibliographic record
Abstract
OBJECTIVE: Preterm birth (PTB) is a prevalent public health concern. Pregnancy anxiety, poor sleep quality, and short sleep duration have been associated with an increased risk of PTB. Theoretically, sleep variables could moderate the strength of the relationship between pregnancy anxiety and PTB; investigating this question was the primary aim of this study. METHOD: = 0.99). Pregnancy anxiety was assessed with the Pregnancy-Related Anxiety Scale, sleep quality was assessed by the Pittsburgh Sleep Quality Index, and sleep duration was assessed via actigraphy. Data on gestational age at birth were obtained from the electronic medical record. RESULTS: After adjustment for relevant covariates, higher levels of pregnancy anxiety were associated with shorter gestational length and an increased risk of PTB. There were no direct associations between sleep quality or sleep duration and gestational length or PTB. Pregnancy anxiety interacted with sleep duration such that pregnancy anxiety was significantly associated with shorter gestational length and PTB only when women had relatively shorter sleep duration (approximately < 8.3 hr). CONCLUSIONS: This study reveals new evidence of an interaction between pregnancy anxiety and sleep duration in the prediction of the timing of delivery. The findings point to avenues to better understand and potentially ameliorate risk for PTB. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".