Use of an Experiential Learning Assignment to Prepare Future Health Professionals to Utilize Social Media for Nutrition Communications
Bibliographic record
Abstract
Social media has become a popular platform for reputable health organizations to disseminate health information to the public. However, future health professionals may receive little training in social media communication. To train future dietetic professionals, we incorporated a social media assignment into a Communications course curriculum to facilitate effective use of social media for the profession. For the assignment, students were instructed to make 2 posts on Facebook. The posts were due 3 weeks apart so that students received feedback on their first post before making their second post. To demonstrate the type of social media communication commonly used by reputable health organizations, the first post raised awareness or provided nutrition education. The second post used Facebook's "comment" feature, to respond to another student's first post, demonstrating the use of social media for community engagement. Both posts included a hyperlink that the user could click to get more information. Students were evaluated on the hook, main points, professionalism, credibility, and effectiveness of inviting the reader to the hyperlinked website and its ease of navigation. Dietetics educators should be encouraged to incorporate social media education into their curriculums for the benefit of future dietitians and their clients.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".