Online Social Engagement by Cancer Patients: A Clinic-Based Patient Survey
Bibliographic record
Abstract
BACKGROUND: The Internet is commonly used as a source of health information, but little is known about the Internet practices specific to cancer patients. OBJECTIVE: To understand cancer patients' use of the Internet as an informational resource and for social support. METHODS: The researchers conducted a survey of 1282 patients at a comprehensive cancer center to assess frequency of Internet access and online behaviors. RESULTS: Of the cancer patients surveyed, 1096 (85.49%) had Internet access; of those with Internet access, 953 (86.95%) reported going online at least weekly, and 747 (68.16%) reported daily online activity. Grouping Internet users by their level of online social engagement revealed that out of 1096 users, 331 (30.20%) had not sought out social connections online, 227 (20.71%) had read about experiences from other cancer patients, 410 (37.41%) had also written about their personal experiences, and 128 (11.68%) had participated in a formal online group for cancer patients. Increased online social engagement was associated with an increased perception that the Internet was useful for social support. CONCLUSIONS: Internet use among cancer patients was common, and most patients reported that they found useful information about their cancer diagnosis online. Cancer patients who actively posted or shared content perceived more social support from the Internet than those who used the Internet solely as an informational resource or to read about other cancer patients' experiences. Physicians have a great opportunity to direct users to quality health information on the Web.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".