Investigating the Association of Subjective Numeracy, Interpersonal Communication, and Perceived Discrimination With Watching Health-Related Videos on Social Media Platforms: Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Over the past two decades, use of social media has grown among US adults. Common social media platforms include Facebook, YouTube, Instagram, X, LinkedIn, and TikTok. People proactively use social media for a variety of purposes including searching for health information, peer-to-peer social support, and health-related information sharing. As these platforms often serve as sources of health information, understanding how, if at all, people use them may inform future behavioral interventions delivered via social media. Additionally, a better understanding of social engagement may have implications for public health messaging and patient-centered communication. Objective: Using a nationally representative sample of US adults, we explored how factors including subjective numeracy (ie, ease of understanding medical statistics), interpersonal communication with family and friends, and perceived discrimination influence whether people ever watched versus never watched health-related videos on social media platforms. Methods: We analyzed the National Cancer Institute's Health Information National Trends Survey data, which were collected from March to November 2022 (n=6252). After excluding participants who did not have complete data for all variables of interest, we analyzed responses from 4543 participants. Respondents were asked, "In the past 12 months, how often did you watch a health-related video on a social media site (eg, YouTube)?" Response options included: almost every day, at least once a week, a few times a month, less than once a month, and never. We collapsed answers into ever or never watched. Odds ratios (OR), 95% CIs, and P values were calculated. A multivariate logistic regression model was considered using all factors that were univariately significant (P<.10). Using backward elimination, factors that were not significant with P>.05 were removed one by one until remaining factors were all significant collectively (P<.05). Results: Of 4543 adults analyzed, 61.5% reported watching at least one health-related video in the past 12 months, whereas 38.5% had never watched one. In the multivariable analysis, all age group categories over 50 years were less likely to watch health-related videos compared to those aged 18-34 years, with respondents aged ≥75 years having the lowest odds of all groups for watching a health-related video (OR 0.16, P<.001). Higher odds of watching health-related videos were observed among respondents who were Black (OR 1.59, P<.01), Hispanic (OR 1.54, P=.01), and from "Other" minority groups (OR 2.07, P=.01) compared to White respondents. College graduates (OR 1.71, P<.01) and those who found medical statistics easy to understand (OR 1.29, P=.04), talked about health with friends or family (OR 1.68, P<.01), or experienced racial discrimination in medical care (OR 1.59, P=.02) also had higher odds of watching health-related videos on social media. Conclusions: Findings from this study may help target health communication campaigns on social media designed to improve screening, lifestyle changes, medication adherence, and disease management.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".