Demographic, Medical, and Psychosocial Predictors of Pregnancy Anxiety
Bibliographic record
Abstract
BACKGROUND: Pregnancy anxiety is associated with risk of preterm birth and an array of other birth, infant, and childhood outcomes. However, previous research has not helped identify those pregnant women at greatest risk of experiencing this specific, contextually-based affective condition. METHODS: We examined associations between demographic, medical, and psychosocial factors and pregnancy anxiety at 24-26 weeks of gestation in a prospective, multicentre cohort study of 5271 pregnant women in Montreal, Canada. RESULTS: Multivariate analyses indicated that higher pregnancy anxiety was independently related to having an unintended pregnancy, first birth, higher medical risk, and higher perceived risk of complications. Among psychosocial variables, higher pregnancy anxiety was associated with lower perceived control of pregnancy, lower commitment to the pregnancy, more stressful life events, higher perceived stress, presence of job stress, lower self-esteem and more social support. Pregnancy anxiety was also higher in women who had experienced early income adversity and those who did not speak French as their primary language. Psychosocial variables explained a significant amount of the variance in pregnancy anxiety independently of demographic and medical variables. CONCLUSIONS: Women with pregnancy-related risk factors, stress of various kinds, and other psychosocial factors experienced higher pregnancy anxiety in this large Canadian sample. Some of the unique predictors of pregnancy anxiety match those of earlier US studies, while others point in new directions. Screening for high pregnancy anxiety may be warranted, particularly among women giving birth for the first time and those with high-risk pregnancies.
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 | 0.001 | 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".