Did COVID-19 challenges psychological resilience of pregnant women? an evidence-based review with recommendation
Bibliographic record
Abstract
Any conflict, extreme stress, emergency situation, natural disaster multiplies mental health hazard. History of Spanish flu outbreak witnesses the damage of pregnant women i.e. as short-term impact inflate the rate of preterm deliveries and the baby’s who were in womb persist the risk of developing medical and psychiatric disorders like diabetes, coronary artery disease, cancer and schizophrenia in future. Pregnant women are considered more vulnerable for COVID-19 as pregnancy makes women prone to respiratory pathogen, which leads to severe pneumonia. Women are three times more prone to anxiety than man. Continuous strict restriction on consultancy visit and gathering, rumors and contradictory information, uncertainty about delivery plan & health of mother and baby indirectly affected women’s emotional and psychological health of perinatal period. Fear and stigma grasps them when anticipating social discrimination and segregation from baby if they become positive. Growing evidence shows psychological impacts i.e. high levels of anxiety, depression and stress are prevalent among pregnant women irrespective of geographical and cultural boundaries across countries like India, China, Canada, UK, Australia and Israel. WHO recommended for adopting holistic approach of care, consideration of major two aspects (i.e. clinical and psychological experiences) in pandemic situations for helping in better positive coping of mother, baby and family members. This present review aimed to find out triggering factors, challenges, major types of psychological issues, consequences of psychological impact among perinatal women due to COVID-19 and want to prescribe evidence-based resolution and preparedness for combating such pandemic situation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".