The COVID-19 Pandemic Significantly Impacts Pregnancy Planning and Mental Health of Women With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND GOALS: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted daily life, particularly in those with inflammatory bowel disease (IBD). We aimed to determine the impact of the pandemic on the pregnancy planning and mental health of women with IBD. METHODS: Women with IBD (age 18 to 45 y) were asked to complete anonymous surveys on the impact of the COVID-19 pandemic on pregnancy planning and mental health symptoms such as stress (Perceived Stress Scale), depression (Patient Health Questionnaire-9), and anxiety (Generalized Anxiety Disorder-7). Univariate and multivariable analyses were conducted to identify risk factors associated with stress, depression, and anxiety during the pandemic. RESULTS: Seventy-three women with IBD were included (mean age: 32.1). Of 39 patients who were preconception, 20 (51.3%) reported a significant impact of the pandemic on pregnancy planning, with common reasons for not planning conception being fear of transmission of the virus to the fetus, lack of social supports, and no desire to be in hospital during pregnancy. Over half of all women reported an increase in stress and depression symptoms during the pandemic, with over half also reporting symptoms of anxiety. On multivariable linear regression analyses, increased anxiety levels were a significant predictor of increased stress and depression symptoms during the pandemic. Urban residence and younger age were significant predictors of increased anxiety symptoms during the pandemic. CONCLUSION: A significant proportion of women with IBD experienced an impact of the COVID-19 pandemic on pregnancy planning and mental health illnesses such as stress, depression, and anxiety.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".