Clarifying longitudinal associations between maternal mental health, perceived sleep quality, and perceived parental impact: A cross-lagged panel model
Bibliographic record
Abstract
• Higher maternal symptoms were linked to poorer sleep quality, but not the reverse. • Higher maternal symptoms were linked to lower perceived parental impact. • Bidirectional links between depressive symptoms and perceived parental impact. • Addressing maternal mental health during the perinatal period is key. • Maternal mental health interventions must address links to perceived parental impact. Pregnancy is a period of heightened vulnerability to mental illness. Research shows that poor sleep quality can predict mental health problems over time, although there is evidence of bidirectional associations. The objective was to study bidirectional links between maternal mental health, sleep quality and perceived parental impact over the perinatal period. Pregnant individuals ( M age =31.13; SD =4.28) were recruited from October 2020-September 2022 for the Resilience and Perinatal Stress during the Pandemic (RESPPA) Study. Analyses ( N = 1385) were conducted using cross-lagged panel models, with data collected from the third trimester of pregnancy (T1), at 3 months postpartum (T2) and 12 months postpartum (T3). Greater maternal anxiety symptoms at T1 were associated with poorer sleep quality at T2 (β=0.168, SE=0.035, p <.001), and with a lower sense of perceived parental impact at T2 (β=-0.141, SE=0.043, p =.001). Greater maternal depressive symptoms at T1 were associated with poorer sleep quality at T2 (β=0.151, SE=0.036, p <.001), and with a lower sense of perceived parental impact at T2 (β=-0.121, SE=0.044, p =.006). Greater maternal depressive symptoms at T2 were associated with a lower sense of perceived parental impact at T3 (β=-0.147, SE=0.039, p <.001). A lower sense of perceived parental impact at T2 was associated with greater maternal depressive symptoms at T3 (β=-0.102, SE=0.031, p =.001). Results indicate associations between maternal mental health symptoms and subsequent perceived sleep quality, but not vice-versa. While anxiety and depressive symptoms were associated with subsequent perceived parental impact, perceived parental impact was also associated with subsequent depressive symptoms (but not anxiety symptoms).
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".