Investigating the Use of Social Networking Tools Among Medical Students
Bibliographic record
Abstract
Background: Social networking tools are often used in medical education to facilitate teaching, owing to their popularity amongst medical students.This study aimed to determine which tools are most widely used by medical students, particularly for educational purposes, to inform future implementation in medical education.Methods: Preclerkship University of Ottawa medical students were surveyed (response rate n=65/325) regarding the use of social networking tools, including Facebook ® , Twitter ® , YouTube ® , Google+ ® , Skype ® , text messaging, blogs, Flickr ® and Pinterest ® .Results: Overall, 85% of respondents use social networking tools for 2 or more hours a day.The tools utilized most frequently on a daily and weekly basis were Facebook (56%) and YouTube ® (40%), respectively.Facebook ® (53%) and YouTube ® (31%) were the most popular tools used specifically for educational purposes, facilitating learning related to lectures and physician skills development, respectively.Conclusion: The majority of students are using social networking tools, but there is some variability in how the tools are used.The variability should be considered when creating educational initiatives.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".