Using Social Media as a Platform to Promote Nutrition Messaging of FLIP
Bibliographic record
Abstract
Social media is an ever growing and versatile medium that presents a novel avenue for health promotion and sharing health information. Despite its growth and popularity, literature is limited on how social media can be used for health promotion interventions, particularly related to food and nutrition. The Food Literacy Intervention Program (FLIP) at the University of Alberta is an evidence-based tailored program targeted at families with children aged 3 to 5. FLIP was designed based on the results of a needs assessment to promote healthy eating in children and improve food knowledge and skills of families. In previous years, FLIP was primarily an in-person cooking class for families and children with a limited online component. Parents had expressed a desire for quick and easy access to credible online nutrition. Therefore, over the course of a 12-week MPH practicum, the online FLIP program was developed and implemented for 2019. The goal of the online program was to explore different forms of social media as a platform to reach a larger audience for the promotion of evidence-based nutrition content. Content was produced for Instagram, Facebook, and a blog, and varying strategies were used to tailor the content to each platform. Social media metrics, including reach and engagement, were captured and analysed to evaluate the effectiveness of the program. This project contributes to the knowledge and understanding of the role of social media for promoting health messages and provides a basis upon which future projects can be created.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".