Knowledge, Attitude, and Practices Among Lebanese Obstetricians and Gynecologists With Respect to COVID-19 and Pregnancy
Bibliographic record
Abstract
Background The COVID-19 pandemic has seriously disrupted the daily life of the general population, particularly the life of pregnant women. Since obstetricians and gynecologists (OBGYNs) are often the primary health care providers during pregnancy, they play a critical role in preventing and managing COVID-19 in their patients. Objective This study aimed to assess the knowledge, attitudes, and practices of OBGYNs with respect to COVID-19 and to identify existing gaps that need to be addressed to improve patient and occupational safety. Methods A cross-sectional study using a web-based survey was conducted among Lebanese OBGYNs during the rapid growth of the COVID-19 pandemic in Lebanon between October 20 and November 20, 2020. The analysis was performed using the SPSS software. Knowledge, attitude, and practice scores were computed. A good level of knowledge was considered when 80% of answers from the respondents were correct. Results A total of 279 OBGYNs participated in the survey, of whom 57% were men. The majority of OBGYNs (64.2%) were more than 45 years of age and married (79.9%) and had extensive work experience (70.3%). Only 28.3% were reluctant to provide medical care for patients with COVID-19. Most of them were afraid of contracting COVID-19 or transmitting COVID-19 contracted through occupational exposure to their family members and 42.3% felt overwhelmed. Of the OBGYNs, 62.7% considered the policies implemented by the Ministry of Public Health to be sufficient. The majority of OBGYNs had a good level of knowledge in different basic and specific domains related to COVID-19 and pregnancy. Furthermore, the practice score was good in all relevant aspects (personal, clinic, and patient). Conclusions The high knowledge and practice scores among Lebanese OBGYNs indicate a strong commitment from these physicians to fulfill their responsibilities toward themselves and their patients during the COVID-19 pandemic.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".