A Social Media Campaign and Web-Based Survey About Prostate Cancer Genetics: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Germline genetic variants are important for prostate cancer (PCa) management and hereditary cancer risk assessment, but testing is underused. Furthermore, patients are often unaware of the genetic connections to PCa. Social media is increasingly serving as a source of awareness for health information and a method to gather data from a large population. OBJECTIVE: There were three objectives: to (1) create and test social media messages related to PCa genetics and genetic testing, (2) determine which social media message was most engaging, and (3) assess knowledge of and attitudes toward PCa genetic testing through an online survey using the most engaging social media message. METHODS: A paid social media campaign was developed to disseminate targeted messages about PCa and genetics. We tested combinations of 8 images and 8 messages that were created or selected by the research team and reviewed by a study-specific advisory board. We targeted men and women older than 35 years living in the United States. The campaign was launched on Facebook for 6 days (June 3-8, 2023). We tracked the reach and impressions of each post. The survey, administered directly after someone viewed a post, assessed knowledge about PCa and cancer genetics as well as beliefs about cancer risk and genetic testing. Descriptive and multivariable analyses were used to analyze survey data. RESULTS: Most posts were viewed by women (13,675/16,224, 84.3% of impressions) and people over the age of 55 years (19,997/22,906, 87.3% of impressions). The 2 most engaging images were a group of men of different races and ethnicities (reach: 28,151 people; impressions: 33,727 views), followed by a Hispanic family (reach: 16,026 people; impressions: 20,113 views). The following message had the most engagement: "Breast cancer and prostate cancer may be related because they can arise from the same gene mutation in a family" (reach: 58,980 people; impressions: 74,834 views). A total of 875 people (n=796, 91% male; mean age 43.42, SD 14.1 years; n=224, 25.6% Black or African American individuals; n=255, 29.1% Hispanic individuals) completed the survey. In total, 75.2% (658/875) strongly or somewhat agreed that genetics play a role in the development of PCa, and 84% (735/875) would want to know if they had a genetic predisposition to PCa. CONCLUSIONS: It is feasible to use social media platforms to test and disseminate messages that raise awareness about PCa genetics and the connection with other cancers (eg, breast cancer), as well as to deploy surveys that reach a wide audience.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".