The Psychological Effects of the Coronavirus Disease 2019 Pandemic on Pregnant and Postpartum Women
Bibliographic record
Abstract
Background: There is little research available regarding the psychological effect of the coronavirus disease 2019 (COVID-19) pandemic on antenatal and postnatal women in the Middle East. This study aimed to evaluate the burden of the pandemic on the mental health of pregnant and postpartum women in Oman. Methods: A cross-sectional study was carried out from July to December 2020 at the Sultan Qaboos University Hospital in Muscat, Oman. A previously validated Arabic version of the Depression, Anxiety, and Stress Scale was distributed to eligible participants via text message. Results: A total of 148 women completed the questionnaire (response rate: 12.8%). Of these, 35 participants (23.6%) reported symptoms of stress, ranging in severity from mild (n = 13, 8.8%) to extremely severe (n = 4, 2.7%); and 44 women (29.7%) reported some level of anxiety, most usually of moderate severity (n = 15, 10.1%). In addition, 46 women (31.1%) reported symptoms of depression, with 16 women (10.8%) having severe or extremely severe depression. Various factors were significantly associated with anxiety and depression levels, including lack of social support due to quarantine restrictions, the presence of an elderly individual at home, and the effect of the pandemic on monthly income (P < 0.05). Conclusions: These findings underscore the importance of finances, family, and social support on the psychological wellbeing of pregnant and postpartum Omani women during this pandemic. In future, healthcare providers should implement awareness campaigns and educational programs to provide additional support to this population group during similar health crises. J Clin Gynecol Obstet. 2021;10(3):73-80 doi: https://doi.org/10.14740/jcgo752
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".