Comparison of health care-related social media and Internet usage between patients treated with curative and palliative intent.
Bibliographic record
Abstract
173 Background: Cancer patients (pts) are increasingly searching online for information and support. Online resource usage and preferences may differ between patients treated with curative versus palliative intent. Methods: Cancer pts completed a cross-sectional survey at Princess Margaret Cancer Centre, assessing their usage and perceptions of social media and the internet with regards to their cancer. Associations between patients’ responses and treatment intent were evaluated univariably (t-tests, chi-squared tests) and multivariably (linear/logistic regression). Results: In a univariable analysis comparing 65 palliative pts (PALL) and 222 curative pts, PALL were more likely to be older (p < 0.001) and less likely to be currently employed or a student (p < 0.001); they were less likely to use the internet (91% vs. 97%, p = 0.03), social media (68% vs. 87%, p < 0.001), and used social media less frequently than curative patients (66% vs. 83%, p = 0.01). PALL were less likely to be interested in an online personal health record (62% vs 76%, p = 0.04) and more likely to indicate that they would not use online information (17% vs. 7%, p = 0.02), compared to curative pts. PALL were more likely to be unfamiliar with social media (20% vs. 7%, p = 0.01), to not know how to use social media (21% vs. 7%, p < 0.001), and to have difficulty finding information (14% vs. 5%, p = 0.03). However, no significant differences by intent were identified after adjustment in a multivariable analysis controlling for age. Conclusions: After the age differences between both groups were adjusted for, there were no significant differences in patients’ online activity nor their perceptions of the trustworthiness, utility, and role of social media, and the Internet. These similarities suggest that online resources for PALL can be developed simultaneously with curative pts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".