Social Media and Health Education: What the Early Literature Says.
Bibliographic record
Abstract
Social media allows for a wealth of social interactions. More recently, there is a growing use of social media for the purposes of health education. Using an adaptation of the Networked student model by Drexler (2010) as a conceptual model, this article conducts a literature review focusing on the use of social media for health education purposes. The review found evidence of the phenomenon, allowing for a discussion surrounding the implications of social media with a health education perspective. Major benefits and risks of social media from a health education perspective are also discussed. Resume Les medias sociaux permettent la realisation d'une multitude d'interactions sociales. Plus recemment, on constate une utilisation croissante des medias sociaux a des fins d'education a la sante. Le present article passe en revue la litterature en mettant l'accent sur l'utilisation des medias sociaux a des fins d'education a la sante et emprunte, comme modele conceptuel, une adaptation du modele des etudiants en reseau (Networked students model) de Drexler (2010). La revue de litterature a demontre l'existence du phenomene et a ainsi donne lieu a une discussion des implications liees aux medias sociaux dans le contexte de l'education a la sante. On y discute egalement des avantages et risques majeurs lies aux medias sociaux dans le contexte de l'education a la sante.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".