Affective Instability, Depression, and Anxiety Symptoms in a Community Sample of Pregnant and Postpartum Women: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Although perinatal women experience an elevated level of affective instability (AI), limited research has been conducted to examine perinatal AI and its relation to depression and anxiety. The current study investigated correlations between AI and depression, between AI and anxiety during the perinatal period, and between current depression and anxiety and the latent factors of the Affective Lability Scale-18 (ALS-18). METHODS: 202 Canadian perinatal women participated in this cross-sectional study. The ALS-18 was used to assess AI, while the Depression, Anxiety, and Stress Scale-21 measured depression, anxiety, and stress. Multiple logistic regression was performed to investigate the relationship between AI and depression and anxiety, and multiple linear regression was conducted to examine the association between current depression and anxiety and the three latent factors of ALS-18. RESULTS: The findings revealed a significant association between AI and depression and between AI and anxiety. Current depression and anxiety were correlated with ALS-18 factors of depression/anxiety shift and depression/elation shift, while current depression was linked to ALS-18 factor of anger. CONCLUSIONS: The study findings have important implications for early detection and intervention of maternal anxiety and depression. In order to improve maternal mental health, AI should be included in routine perinatal check-ups.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".