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.
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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 | 0.002 | 0.002 |
| 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) | 0.004 | 0.001 |
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".