Childbirth fear in the USA during the COVID-19 pandemic: key predictors and associated birth outcomes
Bibliographic record
Abstract
Abstract Background and objectives Childbirth fear, which has been argued to have an adaptive basis, exists on a spectrum. Pathologically high levels of childbirth fear is a clinical condition called tokophobia. As a chronic stressor in pregnancy, tokophobia could impact birth outcomes. Many factors associated with tokophobia, including inadequate labor support, were exacerbated by the COVID-19 pandemic. Methodology We used longitudinally collected data from a convenience sample of 1775 pregnant persons in the USA to evaluate the association between general and COVID-19 pandemic-related factors and tokophobia using the fear of birth scale. We also assessed associations between tokophobia, low birth weight and preterm birth when adjusting for cesarean section and other covariates among a subset of participants (N = 993). Results Tokophobia was highly prevalent (62%). Mothers who self-identified as Black (odds ratio (OR) = 1.90), had lower income (OR = 1.39), had less education (OR = 1.37), had a high-risk pregnancy (OR = 1.65) or had prenatal depression (OR = 4.95) had significantly higher odds of tokophobia. Concerns about how COVID-19 could negatively affect maternal and infant health and birth experience were also associated with tokophobia (ORs from 1.51 to 1.79). Tokophobia was significantly associated with increased odds of giving birth preterm (OR = 1.93). Conclusions and implications Tokophobia increases the odds of preterm birth and is more prevalent among individuals who are Black, have a lower income, and have less education. Tokophobia may, therefore, be an underappreciated contributor to inequities in US birth outcomes. The COVID-19 pandemic likely compounded these effects.
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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 | 0.003 |
| 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 | 0.001 |
| 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".