Facilitating patient communication through understanding their social media use: A comparison by age groups.
Bibliographic record
Abstract
71 Background: Social media and internet is increasingly used by patients for cancer education, which can affect provider-patient communication. Usage habits of the adolescent-young adult (AYA; aged < 40 years), adult (age 40- < 65 years), and geriatric cancer populations (age 65+ years) are likely different. Methods: Using age-specific sampling, cancer patients across all disease sites cross-sectionally were asked to complete a survey of demographics, health status, and social media/online resource use for cancer education. Clinical information was abstracted. Results: Of 429 approached, 320 participated (126 AYA, 128 adults, 66 elderly). Males comprised 44%; 72% had post-secondary education; 31% had household incomes of > $100,000. Elderly patients were most likely to refuse participation (33% of elderly approached vs 16% AYA; p < 0.001), with the most common reason being "I do not use internet resources/don't plan on using them"(96% of all elderly refusals with available data). Among respondents, the proportion who utilized the internet for cancer education was 76%, 76% and 70% in AYA, adults, and elderly, respectively (p > 0.5). The use of social media tools in respondents was 49%, 40%, and 36%, respectively (p = 0.16 across age groups). While 75% of patients felt they could judge the quality of cancer-related information on the internet (no differences by age group, p > 0.5), a significantly lower 43% (p < 0.001) felt similarly confident to judge the quality of social media; AYA patients (49%) were numerically more likely to feel confident than seniors (36%; p = 0.16). Elderly were less likely to want online health record access (p = 0.015), treatment option (p = 0.042) and side effect education (p < 0.001), future care plan (p < 0.001) and wellness programs compared to others (p < 0.001). Conclusions: Although cancer patients used social media frequently, confidence is lacking on the quality of cancer information obtained (across all age groups), while elderly perceive fewer benefits of using online/social media related to their cancer. Guidelines for patients on how to assess quality and appropriately use social media could help facilitate patient-provider communication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".