Understanding preferences for receiving health communications and information about clinical trials: a cross-sectional study among US adults
Bibliographic record
Abstract
OBJECTIVE: Effective health communication is critical for understanding and acting on health information. This cross-sectional study explored participants' understanding of their health condition, their preferences for receiving health communications, and their interest in receiving clinical trial results across several therapeutic areas. METHODS: social media, email newsletters, and advocacy organizations. An online screener captured demographic information (health conditions, age, race/ethnicity, gender, and education). Eligible participants were emailed an online survey assessing preferred sources and formats for receiving health information, interest in learning about topics related to the results of clinical trials, and health literacy levels. RESULTS: In total, 449 participants (median age, 35 years [range, 18-76]; White, 53%; higher education, 65%; mean (range) health literacy score, 1.9 [0.4-3.0]) from 45 US states completed the survey representing 12 disease indications (bipolar, blood and solid tumor cancers, irritable bowel syndrome, inflammatory bowel disease, major depressive disorder, migraine, Parkinson's, psoriasis/atopic dermatitis, retinal vein occlusion/macular degeneration, rheumatoid arthritis, and spasticity). Healthcare providers were the preferred source of health information (59%), followed by Internet searches (11%). Least preferred sources were social media (5%), friends/family (3%), and email newsletters (2%). Participants preferred multiple formats and ranked reading materials online as most preferred (33%), along with videos (28%) and infographics (27%). Printed materials (14%) and audio podcasts (9%) were the least preferred formats. A majority of the participants reported that the health information they found was hard to understand (57%) and confusing (62%). Most participants (85%) were somewhat/very interested in learning about clinical trial results, with the highest interest in short summaries of safety (78%) and efficacy (74%) results. CONCLUSION: multiple formats shared directly by healthcare providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| 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 | 0.001 |
| 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".