Supporting Cancer Patients through the Continuum of Care: A View from the Age of Social Networks and Computer-Mediated Communication
Bibliographic record
Abstract
Almost since its inception, the Internet has been used by ordinary people to connect with peers and to exchange health-related information and support. With the rapid development of software applications deliberately designed to facilitate social interaction, a new era is dawning in which patients and their loved ones can collaboratively build knowledge related to coping with illness, while meeting their mutual supportive care needs in a timely way, regardless of location. In this article, we provide background information on the use of "one-to-one" (for example, e-mail), "one-to-many" (for example, e-mail lists), and "many-to-many" (for example, message boards and chat rooms, and more recently, applications associated with Web 2.0) computer-mediated communication to nurture health-related social networks and online supportive care. We also discuss research that has investigated the use of social networks by patients, highlight opportunities for health professionals in this area, and describe new advances that are fuelling this new era of collaboration in the management of cancer.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".