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.
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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".