2020 Vision: improving supportive and palliative care in the age of social media and global telecommunications
Bibliographic record
Abstract
Abstract In the second decade of the twenty-first century, social media are ushering a second wave in the evolution of the Web. Very rapidly, applications such as Google, Wikipedia, Facebook, YouTube and Twitter have risen to be among the most used sites on the Web, re-shaping how humans communicate, learn and live. Mobile communication devices are converging with Internet-based services, penetrating every region of the planet at a speed that dwarfs the growth in the adoption of personal computers or any other preceding technological innovation. These devices can now access hundreds of thousands of applications directly through the Internet, promising to satisfy almost any human need for information and communication. The exponential pace of evolution of information and communication technology, however, is outpacing the ability of clinicians, researchers, managers and policy makers to keep up. As a generation with the rare privilege to witness the emergence of a new set of powerful technologies that could have a profound and widespread effect on society, we must look beyond the hype, and try our best to understand what works, what does not work and what could be harmful. This session will give participants an opportunity to learn about emerging innovations in social media that could enable us to reduce unnecessary suffering. It will also underscore key methodological, political, cultural, technological and financial challenges that must be addressed urgently if we are to harness their power to improve the way in which we design, develop, provide, receive and evaluate supportive and palliative care services.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".