Harnessing the Social Web for Health and Wellness: Issues for Research and Knowledge Translation
Bibliographic record
Abstract
Social media is a powerful, rapid, and popular way of communication amongst people around the world. How can health professionals and patients use this strategy to achieve optimal disease management and prevention and attainment of wellness? An interdisciplinary group at University of British Columbia, supported by a grant from UBC Peter Wall Institute of Advanced Studies, conducted a research workshop in February 2013 to explore what is known and yet to be researched in using social media for nurturing the growth of virtual communities of people for health and wellness. This two and a half day workshop brought together a group of 30 multidisciplinary experts in closed discussions to reflect on five research themes in detail: (1) individual information acquisition and application, (2) community genesis and sustainability, (3) technological design issues, (4) knowledge management, dissemination, and renewal, and (5) research designs. In addition, a public forum for the general public, which attracted over 195 live participants, over 100 participants via Web casting, 1004 tweets, and 1,124,886 impressions following the #HCSMForum hash tag on Twitter, demonstrated the keen interest of the general public in this topic. Key concepts were captured in JMIR publications in this issue, and future directions, including research, knowledge translation approaches, and strategic partnerships of interdisciplinary researchers with policy makers and industries emerged from the workshop proceedings.
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 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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".