COVID-19 knowledge, awareness and perceived stress among Jordanian healthcare providers: An online Cross-sectional Study (Preprint)
Bibliographic record
Abstract
Background: Healthcare providers (HCPs) are the frontline workers amidst the COVID-19 pandimic and they are potentially in direct contact with infected patients.Thus, they are prone to a many adverse consequences such as getting infected and psychological stress.Objective: To measure levels of knowledge, awareness, and stress about COVID-19 among health care providers (HCP) in Jordan. Methods:To measure levels of knowledge, awareness, and stress about COVID-19 among health care providers (HCP) in Jordan.Results: Overall, 97 (24.4%) showed excellent knowledge, while 216 (54.4%) and 84 (21.2%) demonstrated good and poor knowledge, respectively.Social media (61.7%) and medical papers (57.7%) were the most commonly used sources of information.Being female (?= 0.521, 95% CI 0.049 to 0.992), a physician (?=1.421, 95% CI 0.849 to 1.992), or using published literature to gain knowledge (?= 1.161, 95% CI 0.657 to 1.664) were positive predictors of higher knowledge levels.While having higher levels of stress (?= -0.854, 95% CI -1.488 to -0.221) and using social media (?= -0.434, 95% CI -0.865 to -0.003) or conventional media (?= -0.884, 95% CI -1.358 to -0.409) to gain information were negative predictors of knowledge levels.The availability of N95 masks (33.5%) and disposable eye protectors or face shields (26.7%) was significantly associated with lower psychological stress (P=.01).Conclusions: HCPs are advised to use the published literature as a source of information about the virus, its transmission, and the best practice to attain sufficient knowledge regarding COVID-19.PPEs should be secured for HCPs to the psychological stress associated with treating COVID-19 patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".