Current COVID-19 Pandemic and Medical Education: Medical Students' Perception and Experiences with Online Clinical Teaching and Learning at College of Medicine in Oman
Bibliographic record
Abstract
Background: Clinical teaching is a form of interpersonal communication between a teacher and learner. It mainly involves a patient or a patient scenario; the student learns how to evaluate a patient and manage the problem. The ideal clinical teaching and learning are done in the patient care area, but because of the COVID-19 pandemic outbreak, all clinical and classroom teaching is suspended now.Objective: This study's main purpose was to assess medical students' perceptions and experiences with online clinical teaching and learning.Method: A cross-sectional study was conducted at the College of Medicine and Health Sciences (CoMHS). All students in 6 and 7 years consented to participate in the survey for a self-filled study (google form). Statistical analysis was performed using Statistical Package for Social Sciences (IBM SPSS Statistics 24.0). Data were expressed in frequencies for questionnaire responses calculated for all variables in numbers and percentages. Independent sample t-test was used to compare differences between two groups. Result: Ninety-one students participated in the study, of which 10.2% were male, and 46.2% were Omani citizens. 27.5% of students were 6th year, and 72.5% were 7th-year students. 69.2% of students did not experience any login/registration problem in GoToWebinar most of the time. Table 1 shows the student's responses in this regard. A significant statistical difference (p- <0.001; 95 % CI: 0.34-0.91) was observed between 6th year (mean-2.79±0.62) and 7th year students (mean-2.16±0.51). Conclusion: Medical students have shown a positive attitude and motivation towards webinar clinical teaching. Online webinar teaching can offer more diverse and compelling educational opportunities. Medical students in clinical years are self-directed learners but need in-depth learning with maximum hands-on practice. The Webinar teaches an impact on medical student education, particularly affecting the hands-on approach and training, which is limited and mandatory to become a doctor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".