Impelling Factors for Contracting COVID-19 Among Surgical Professionals During the Pandemic: A Multinational Cohort Study
Bibliographic record
Abstract
Background: Medical workers, including surgical professionals working in coronavirus disease 2019 (COVID-19) treating hospitals, were under enormous stress during the pandemic. This global study investigated factors endowing COVID-19 amongst surgical professionals and students. Methods: This global cross-sectional survey was made live on February 18, 2021 and closed for analysis on March 13, 2021. It was freely shared on social and scientific media platforms and was sent via email groups and circulated through a personal network of authors. Chi-square test for independence, and binary logistic regression analysis were carried out on determining predictors of surgical professionals contracting COVID-19. Results: This survey captured the response of 520 surgical professionals from 66 countries. Of the professionals, 92.5% (481/520) reported practising in hospitals managing COVID-19 patients. More than one-fourth (25.6%) of the respondents (133/520) reported suffering from COVID-19 which was more frequent in surgical professionals practising in public sector healthcare institutions (P = 0.001). Thirty-seven percent of those who reported never contracting COVID-19 (139/376) reported being still asked to practice self-isolation and wear a shield without the diagnosis (P = 0.001). Of those who did not contract COVID-19, 75.7% (283/376) were vaccinated (P < 0.001). Surgical professionals undergoing practice in the private sector (odds ratio (OR): 0.33; 95% confidence interval (CI): 0.14 - 0.77; P = 0.011) and receiving two doses of vaccine (OR: 0.55; 95% CI: 0.32 - 0.95; P = 0.031) were identified to enjoy decreased odds of contracting COVID-19. Only 6.9% of those who reported not contracting COVID-19 (26/376) were calculated to have the highest "overall composite level of harm" score (P < 0.001). Conclusions: High prevalence of respondents got COVID-19, which was more frequent in participants working in public sector hospitals. Those who reported contracting COVID-19 were calculated to have the highest level of harm score. Self-isolation or shield, getting two doses of vaccines decreases the odds of contracting COVID-19.
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 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 | 0.001 |
| 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) | 0.002 | 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".