Exploring mood variability in pregnancy and postpartum women
Bibliographic record
Abstract
Introduction Emotional and moody behaviour is often normalized in childbearing women. However, increased mood variability is associated with psychiatric problems (anxiety, depression, personality disorder), which are potentially deleterious to the health of the developing fetus and mother. Purpose To increase understanding about mood variability in childbearing women. Method Depression was measured using the Edinburgh Postpartum Depression Scale (EPDS). Mood variability was calculated from twice-daily diary ratings of “depressed”, “fear”, and “irritable” mood for one week each in early pregnancy, late pregnancy, and postpartum. Findings We recruited 47 women. Depression, as measured by the EPDS, and fear mood variability decreased from early pregnancy to postpartum. Depressed and irritable mood variability also declined during pregnancy, but increased in post-partum. Increases in mood variability (depressed, irritable) from late pregnancy to postpartum predicted higher postpartum EPDS. Mood diaries were available from 30 non-parturient women for comparison. Pregnant and postpartum women had higher irritable but not depressed or fear mood variability. Conclusion Mood, particularly anxiety, is variable over the course of pregnancy into early postpartum. This may be related to diminishing concerns about the pregnancy and baby's health after birth, sleep disruptions, or hormonal changes. Depressed mood variability is correlated with, but is distinct from depression as measured by the EPDS. We present results counter to the notion that all mood is amplified in childbearing women.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".