eHealth promotion and social innovation with youth: using social and visual media to engage diverse communities.
Bibliographic record
Abstract
Social media and the multimedia networks that they support provide a platform for engaging youth and young adults across diverse contexts in a manner that supports different forms of creative expression. Drawing on more than 15 years of experience using eHealth promotion strategies to youth engagement, the Youth Voices Research Group (YVRG) and its partners have created novel opportunities for young people to explore health topics ranging from tobacco use, food security, mental health, to navigation of health services. Through applying systems and design thinking, the YVRG approach to engaging youth will be presented using examples from its research and practice that combine social organizing with arts-informed methods for creative expression using information technology. This presentation focuses on the way in which the YVRG has introduced interactive blogging, photographic elicitation, and video documentaries, alongside real-world social action projects, to promote youth health and to assist in research and evaluation. Opportunities and barriers including literacy and access to technology are discussed and presented along with emerging areas of research including more effective use of smartphones and social networking platforms such as Twitter, Facebook, and YouTube in health promotion and 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".