Pregnant under the pressure of a pandemic: a large-scale longitudinal survey before and during the COVID-19 outbreak
Bibliographic record
Abstract
BACKGROUND: One of the groups that is most vulnerable to the COVID-19 pandemic is pregnant women. They cannot choose to refrain from care; they and their children are at risk of severe complications related to the virus; and they lose comfort and support as clinics prohibit their partners and as societal restrictions demand isolation from friends and relatives. It is urgent to study how this group is faring during the pandemic and we focus here on their health-related worries. METHODS: A longitudinal survey at a Swedish hospital starting 6 months before (16 September 2019) and continuing during the COVID-19 outbreak (until 25 August 2020). A total of 6941 pregnant women and partners of diverse social backgrounds were recruited. Ninety-six percent of birth-giving women in the city take early ultrasounds where recruitment took place. Sixty-two percent of the women with an appointment and fifty-one percent of all partners gave consent to participate. RESULTS: Pregnant women experienced dramatically increased worries for their own health, as well as for their partner's and their child's health in the beginning of the pandemic. The worries remained at higher than usual levels throughout the pandemic. Similar, but less dramatic changes were seen among partners. CONCLUSIONS: There is a need for heightened awareness of pregnant women's and partners' health-related worries as a consequence of the COVID-19 pandemic. Related feelings, such as anxiety, have been linked to adverse pregnancy outcome and might have long-term effects. The healthcare system needs to prepare for follow-up visits with these families.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".