Using Social Media as a Pedagogical Tool in Graduate Public Health Education and Training
Bibliographic record
Abstract
Public health is a complex field where current information and evidence available to inform best practices are constantly changing. With the rise of social media influencing public health actions, it is becoming more important for those working in the sector to have a proficient understanding of this form of communication. Social media use amongst public health organizations is also on the rise. Twitter, Facebook, and YouTube have been used to disseminate timely information as well as for public health education. We argue that formally integrating social media as a pedagogical tool in public health graduate programs would benefit both, educators and students as well as the public health field in which students will work. Communication skills have been included in core competencies for public health professionals as an essential skill. Critical in that skill is the ability to work with new methods of communication, such as social media. We bring forward the idea that social media should both be used in teaching and taught as an essential skill. Using social media as an educational tool is an opportunity to ensure that graduate public health programs train students with the competencies to work in public health.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".