Social Media as a Platform for Cancer Care Decision-Making Among Women: Internet Survey-Based Study on Trust, Engagement, and Preferences
Bibliographic record
Abstract
BACKGROUND: Decision aids improve patient and clinician decision-making but are underused and often restricted to clinical settings. OBJECTIVE: Given limited studies analyzing the feasibility of disseminating decision aids through social media, this study aimed to evaluate the acceptability, trust, and engagement of women with social media as a tool to deliver online decision aids for cancer treatment. METHODS: To prepare for potential dissemination of a breast cancer decision aid via social media, a cross-sectional survey in February 2023 was conducted via Prime Panels, an online market research platform, of women aged 35-75 years in the United States. Demographics, health, cancer information-seeking behaviors, social media use, trust in social media for health information, as well as the likelihood of viewing cancer-related health information and clicking on decision aids through social media, were assessed. Statistical analyses included descriptive statistics, correlations, and multivariable ordinal regression. RESULTS: Of 607 respondents, 397 (65.4%) had searched for cancer information, with 185 (46.6%) using the internet as their primary source. Facebook (Meta) was the most popular platform (511/607, 84.2%). Trust in social media for health information was higher among Black (14/72, 19.4%) and Asian respondents (7/27, 25.9%) than among White respondents (49/480, 10.2%; P=.003). Younger respondents aged 35-39 years (17/82, 20.7%) showed higher trust than those aged 70-79 years (12/70, 17.1%; P<.001). Trust in social media for health information was linked to a higher likelihood of viewing cancer information and accessing a decision aid online (P<.001). Participants who rated social media as "Trustworthy" (n=73) were more likely to view cancer information (61/73, 83.6%) and click on decision aids (61/73, 83.6%) than those who found it "Untrustworthy" (n=277; view: 133/277, 48.0%; click: 125/277, 45.1%). Engagement with social media positively correlated with viewing online cancer information (Spearman ρ=0.20, P<.001) and willingness to use decision aids (ρ=0.21, P<.001). Multivariable ordinal regression analyses confirmed that perception of social media's trustworthiness is a significant predictor of engagement with decision aids (untrustworthy vs trustworthy β=-1.826, P<.001; neutral vs trustworthy β=-0.926, P=.007) and of viewing cancer information (untrustworthy vs trustworthy β=-1.680, P<.001, neutral vs trustworthy β=-0.581, P=.098), while age and employment status were not significant predictors. CONCLUSIONS: This exploratory study suggests that social media platforms may increase access to health information and decision aids. No significant differences were observed between demographic variables and the use or trust in social media for health information. However, trust in social media emerged as a mediating factor between demographics and engagement with cancer information online. Before disseminating decision aids on social media, groups should identify existing trust and engagement patterns with different platforms within their target demographic.
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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 | 0.002 | 0.010 |
| 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".