The Trends of Use of Social Media by Medical Students
Bibliographic record
Abstract
Introduction: As the online environment has evolved, the use of social networking sites (SNSs) hasbeen integrated into the methods of teaching. Students across the world are currently using SNSs toenhance their learning. Objective: This study sought to explore the students’ use of social media,in particular that of Facebook groups in medical education at the University of Ottawa. Methods:Pre-clerkship medical students (n = 160) were surveyed regarding the trends of use of SNSs in theirlearning. The survey consisted of 23 questions (Likert-style, multiple choice, yes/no, and short answerquestions). Results: 94% of respondents use SNSs to facilitate their learning with Facebook (n = 98,97%). Students mostly use Facebook groups for histology (30%), physiology (21%), etc. They mostlyuse SNSs for these particular subjects because the material posted is engaging. Sixty percent (60%) ofstudents use SNSs to communicate with their colleagues and 59.8% stated that they prefer Facebookgroups over pages. They prefer sample tests/quizzes and study guides (65.6%), followed by explanatorycomments and an answer to a question (54.2%), etc. The downside of the use of social media ineducation is distraction and privacy issues. Conclusion: SNSs are used by the majority of students toenhance their learning, but to use them to their fullest; the material posted has to be concise, engagingand aligned with the learning objectives. Social media are contemporary and efficient communicationtools that educators cannot overlook; the challenge is to choose the right platform, the amount andquality of the information shared to ensure optimal benefit and collaboration of the students.
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 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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".