Latent Trajectory Groups of Maternal Depressive and Anxiety Symptoms and the Associated Risk Factors
Bibliographic record
Abstract
Abstract\nBackground: There is a growing evidence that depression and anxiety disorders have distinct groups of symptom trajectories, which are associated with factors that may vary among different groups. Studying these mental health trajectories is highly relevant during major life transitions, such as pregnancy and childbirth. The aim of this thesis is to identify subgroups of women who exhibit distinct longitudinal trajectory patterns of depressive and anxiety symptoms from early pregnancy to early postpartum and from pregnancy to five years postpartum and the risk factors associated with these trajectories. \nMethods: This study uses a longitudinal data collected from 615 women in Saskatchewan, Canada from pregnancy to five years postpartum between 2006 and 2013 (Feelings in Pregnancy and Motherhood (FIP) longitudinal study). The semiparametric group-based modeling strategy was used to identify the latent groups of maternal depressive and anxiety trajectories. Multinomial logit models were then used to explore the association between these latent trajectory groups and various maternal characteristics.\nResults: Across pregnancy to early postpartum, we identified four trajectory groups of depressive symptoms: low-stable (49.6%); moderate-stable (42.3%); postpartum (3.6%); and antepartum (4.6%), and three latent trajectory groups of anxiety symptoms: very low-stable (8.9%); low-stable (60.7%); and moderate-stable (30.4%). From pregnancy to five years postpartum, four latent trajectory groups of depressive symptoms were identified: low-stable (35.0%); moderate-stable (54.0%); low-rising (5.2%); and high-declining (5.9%), and three latent trajectory groups for anxiety symptoms were identified: very low-stable (13.0%); low-stable (58.1%); and high-stable (29.0%). Several maternal risk factors, most notably past depression and stress level, were associated with these trajectories. \nConclusion: Distinct latent trajectory patterns of maternal depressive and anxiety symptoms were identified, which were associated with different profiles of risk factors present prior to or during pregnancy. Our findings support the need for multiple assessments starting from early pregnancy to the postpartum, which may help to recognize women at high risk of major depression or anxiety. All significant risk factors can be identified during regular follow-up and thus, clinicians may be able to identify women at high risk, who may be potential candidates for early interventions that may alter the progress of their mental health 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".