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.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".