Gravidity status predicts mental health symptoms in women planning a pregnancy
Bibliographic record
Abstract
BACKGROUND: Gravidity is a factor that contributes to the risk of mental health problems during pregnancy. Prevention occurring before pregnancy could reduce the risk of long-term adverse effects in mothers and children. However, knowledge about the associations between gravidity and mental health during the pregnancy planning phase is scarce. METHODS: This study used cross-sectional data from the preconception phase of the PREGO Study. A total of 617 female participants (417 nulligravid, 200 primigravid/multigravid) were included and multivariable regression analyses were used to examine associations between gravidity status (primi/multigravid vs. nulligravid) and symptoms of depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), stress (Perceived Stress Scale), and psychological distress (Kessler Psychological Distress Scale), adjusting for age, family income, education level, ethnic and cultural origins, body mass index, and alcohol consumption. RESULTS: The primi/multigravid group was associated with higher levels of depressive symptoms and distress before and after adjusting for confounding factors (adjusted B = 0.93, 95% CI [0.38-1.47] and adjusted B = 0.61, 95% CI [0.02-1.21], respectively). No statistically significant associations between gravidity and symptoms of anxiety and stress were observed. CONCLUSION: Individuals who have previously been pregnant may have higher levels of depressive symptoms and distress when planning a subsequent pregnancy than those with no prior experience of pregnancy. An awareness of these findings in preconception groups could help improve pregnancy and postpartum mental health outcomes.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".