Attitudes of Health Professional Educators Toward the Use of Social Media as a Teaching Tool: Global Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The use of social media in health education has witnessed a revolution within the past decade. Students have already adopted social media informally to share information and supplement their lecture-based learning. Although studies show comparable efficacy and improved engagement when social media is used as a teaching tool, broad-based adoption has been slow and the data on barriers to uptake have not been well documented. OBJECTIVE: The objective of this study was to assess attitudes of health educators toward social media use in education, examine differences between faculty members who do and do not use social media in teaching practice, and determine contributing factors for an increase in the uptake of social media. METHODS: A cross-sectional Web-based survey was disseminated to the faculty of health professional education departments at 8 global institutions. Respondents were categorized based on the frequency of social media use in teaching as "users" and "nonusers." Users sometimes, often, or always used social media, whereas nonusers never or rarely used social media. RESULTS: A total of 270 health educators (52.9%, n=143 users and 47.0%, n=127 nonusers) were included in the survey. Users and nonusers demonstrated significant differences on perceived barriers and potential benefits to the use of social media. Users were more motivated by learner satisfaction and deterred by lack of technology compatibility, whereas nonusers reported the need for departmental and skill development support. Both shared concerns of professionalism and lack of evidence showing enhanced learning. CONCLUSIONS: The majority of educators are open-minded to incorporating social media into their teaching practice. However, both users and nonusers have unique perceived challenges and needs, and engaging them to adapt social media into their educational practice will require previously unreported approaches. Identification of these differences and areas of overlap presents opportunities to determine a strategy to increase adoption.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".