Correlates of Canadian mothers’ anger during the postpartum period: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Although some women experience anger as a mood problem after childbirth, postpartum anger has been neglected by researchers. Mothers' and infants' poor sleep quality during the postpartum period has been associated with mothers' depressive symptoms; however, links between mothers' sleep quality and postpartum anger are unclear. This study aimed to determine proportions of women with intense anger, depressive symptoms, and comorbid intense anger and depressive symptoms, and to examine mothers' and infants' sleep quality as correlates of postpartum anger. METHODS: This cross-sectional survey study was advertised as an examination of mothers' and babies' sleep. Women, with healthy infants between 6 and 12 months of age, were recruited using community venues. The survey contained validated measures of sleep quality for mothers and infants, and fatigue, social support, anger, depressive symptoms, and cognitions about infant sleep. RESULTS: 278 women participated in the study. Thirty-one percent of women (n = 85) reported intense anger (≥ 90th percentile on State Anger Scale) while 26% (n = 73) of mothers indicated probable depression (>12 on Edinburgh Postnatal Depression Scale). Over half of the participants rated their sleep as poor (n = 144, 51.8%). Using robust regression analysis, income (β = -0.11, p < 0.05), parity (β = 0.2, p < 0.01), depressive symptoms (β = 0.22, p < 0.01), and mothers' sleep quality (β = 0.10, p < 0.05), and anger about infant sleep (β = 0.25, p < 0.01) were significant predictors of mothers' anger. CONCLUSIONS: Mothers' sleep quality and anger about infant sleep are associated with their state anger. Clinicians can educate families about sleep pattern changes during the perinatal time frame and assess women's mood and perceptions of their and their infants' sleep quality in the first postpartum year. They can also offer evidence-based strategies for improving parent-infant sleep. Such health promotion initiatives could reduce mothers' anger and support healthy sleep.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".