Knowledge and Information Sources About COVID-19 Among University Students in Jordan: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, and it was officially declared to be a pandemic by the World Health Organization (WHO) March 11, 2020. Most countries over the entire globe have reported some COVID-19 cases. The current study aimed to assess student knowledge about COVID-19 at different Jordanian universities and determine where they sourced their information. Methods: A cross-sectional study was conducted among 2083 undergraduate or postgraduate students from different governmental and private universities during the initial stage of the disease in Jordan (March 19-21, 2020) using a validated, structured, self-administered, online questionnaire. The survey was structured to assess their knowledge about viral sources, incubation period, mortality rate, transmission, symptoms and complications as well as the source of information about COVID-19. Results: Overall, 56.5% of the respondents showed good knowledge and almost 40.5% showed moderate knowledge. On the other hand, 3.0% of the participants showed poor knowledge about COVID-19. The average knowledge score of students was 80.1%, which is considered to be within the scale of good knowledge. Both the college of study and educational level significantly (P < 0.05) affected student knowledge. Students who majored in medical sciences showed the highest mean score of 82.8%, with 69.0% displaying a good knowledge level. Postgraduate students had significantly higher knowledge scores compared to undergraduate students. The majority of students used the internet, social media and mass media as sources of information about COVID-19. Scientific websites and articles were used more commonly by medical and postgraduate students. Conclusions: The COVID-19 pandemic is a major challenge to the health of the world population; therefore, these results assessing students' knowledge provide an important baseline for planning required educational interventions such as contact tracing and self-quarantine. These results also may help public health authorities by engaging communities in implementation of protective health measures, including positive hygienic practices such as hand washing to reduce the risk of COVID-19.
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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) | 0.001 | 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) | 0.002 | 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".