Knowledge, attitudes, and practices against the growing threat of COVID-19 among medical students of Pakistan
Bibliographic record
Abstract
BACKGROUND: On account of the COVID-19 pandemic, many changes have been implicated in university medical students. We are cognizant that pandemic can be controlled with dedicated contributions from all involved in the healthcare profession. Therefore, it is important to know the pandemic and application of knowledge by the medical students to formulate a further line of management in Pakistan. OBJECTIVE: We aimed toinvestigate the knowledge, attitudes, and practices (KAP) regarding COVID-19 and its impact on medical students of Pakistan. METHODS: A cross-sectional survey was conducted in June 2020 by a validated self-administered questionnaire. The survey instrument was tailored from a published questionnaire comprised of questions on demographics (6), knowledge (14), attitudes (4), and practices (6). RESULTS: Of the 1474 medical students in the study, 576(39.1%) were males, and 898(60.1%) were females. Two-thirds of the participants 1057(71.7%) had adequate knowledge, and almost all the students had positive attitudes (1363(92.5%), and good practices 1406(95.4%) to COVID-19. Two-thirds of the medical students 1023(69%) believed that the COVID-19 outbreak had affected their social, mental, and psychological well-being. One-quarter of the medical students 391(26%) become more religious, 597(40%) realized the importance of life, and 1140(77%) became careless because of the pandemic. The female medical students were 2.545 times (p < .001) and 4.414 times (p < .001) more likely to have positive attitudes and good practices toward COVID-19 as compared to males. CONCLUSION: Medical students, especially females and senior year scholars, were well-versed with desired levels of knowledge, attitudes, and preventive measures toward COVID-19. Most of them recognized COVID 19, is shaping their social, mental, and psychological well-being and encroaching on the healthcare system and economy. The information acquired by the KAP study may help to devise effective preventive strategies for future events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".