Assessing COVID-19 knowledge, attitudes, and practices among hospital employees: identifying sociodemographic determinants for improved public health strategies
Bibliographic record
Abstract
Background The working environment of healthcare institution during pandemic puts all hospital employees at high-risk of being exposed to contagious infections. An individual’s behavior and response are largely determined by their level of knowledge, attitudes and practices (KAP) toward a disease. Therefore, the present study aimed to evaluate and assess the KAP toward COVID-19 among hospital employees working in various positions and to identify the sociodemographic determinants associated with the level of KAP. Methods A cross-sectional survey was conducted from July 1 to July 15, 2020 in Almoosa Specialist Hospital, Alhasa, Eastern Province, Saudi Arabia in which 221 hospital employees with varied job titles participated. The data for demographics and history of COVID-19 exposure, KAP related to COVID-19 spread and prevention were collected online using a web-based platform (Survey Monkey). Student’s t -test/One-way ANOVA were used to compare total mean and standard deviation of KAP scores with demographic profiles and history of exposure. Results 89.1% employees knew that COVID-19 virus is mostly transmitted from human-to-human, and 76.0% employees acknowledged droplet transmission. 64.7% employees preferred to take a sick person with unconfirmed COVID-19 to a health facility. Physicians had higher knowledge scores for COVID-19 infection and non-medical employees had the lowest scores (7.47 ± 1.23 and 6.47 ± 1.44, respectively). Nurses had the highest practice scores and non-medical employees lowest practice score (6.16 ± 0.74 and 5.37 ± 1.14, respectively). Attitude scores were similar among all the employees. All employees reported an increase in hand-washing frequency and physical contact avoidance. Conclusion The study results revealed socio-demographic factors; level of education, nationality, and field of service are associated with COVID-19 KAP. The study highlights that there is a gap in the level of knowledge about COVID-19, especially among nonmedical employees. Targeted interventional programs need to be planned and implemented to improve COVID-19 awareness among non-medical employees.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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".