New Face of Clinical Teaching and Learning: Social Media in Medical Education Use of WhatsApp among Medical Students in Clinical Teaching at Oman Medical College
Bibliographic record
Abstract
Medical education has its core values of confidentiality and formal conduct while social media involved sharing and openness, connection which seems to be contradictory for medical professionalism.Main purpose of this study was to explore the students' perception, attitudes and barriers about the professional use of social media and to assess the experiences of undergraduate on the improvement of clinical teaching through the incorporation of social media applications.A cross sectional survey based study was carried out at Oman Medical College.All final year students consented to participate were included in the study.Data was collected on self-administered questionnaire in which core elements were divideddemographics, type and frequencies of different social media usage, student's perception about WhatsApp utilization and barriers of not using social media.Statistical analysis was performed using SPSS (IBM SPSS Statistics 20.0).Data were expressed in frequencies, mean and percentages.A total of 76 participants were enrolled in which 5 (6.6%) were male and 71 (93.4%) were aged between 20-25 years of age.Among all 57 (75%) were Omani nationals and almost all (98.7%) participants used social media of which 35 (46.1%) were android, 29 (38.2%)IOS, and 12 (15.8%)were other operating system users.All participants daily spend some time on YouTube, Facebook, Twitter and WhatsApp.In contrast, responses of all participants' indicated that they never make use of Wiki, Chat On and hangout.More than half of the study participants believed that lack of internet access is one the main barrier of nonutilization of social media.Medical students prefer online media for communication and medical information along with usage of WhatsApp in medical education and learning is helpful for improving and enhancing the interactive learning.The students' response emphasizes positive response and experiences of their learning and discussions provided an effective space for integrated small group clinical teaching and learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".