Technology Access, Digital Literacy, and Enrollment Support Preferences in a Federally Qualified Health Center: Cross-Sectional Study
Bibliographic record
Abstract
Background: Biomedical research studies are increasingly using digital tools to enroll, recruit, and collect data from participants. However, variability in digital literacy and technological acceptance can be challenging for recruitment from groups traditionally underrepresented in research, including those served by Federally Qualified Health Centers. Objective: This study aimed to (1) measure participant accessibility and comfort with digital platforms and (2) examine the interrelation of technology access, digital literacy, and support preferences during enrollment and data submission. Methods: A cross-sectional analysis was conducted using enrollment data from Federally Qualified Health Centers participating in the All of Us Research Program. Participants had the option of High-Touch (staff-assisted) or Low-Touch (self-directed) support for enrollment and survey completion. Survey items assessed internet access and technology comfort, while support type was recorded by the research staff based on participants' actual selection. Logistic regression models evaluated relationships between technology access, comfort, and enacted support type, while controlling for age, consent language, and education, as well as race and ethnicity. Results: The analytic sample included 605 participants. The majority reported access to the internet (539/605, 89.1%) and felt comfortable with technology (448/605, 74.1%). In the group requesting High-Touch support (n=346), 14.5% (n=50) reported no internet access, and 31.5% (n=109) felt uncomfortable with technology. In the group requesting Low-Touch support (n=259), 6.2% (n=16) had no access to the internet, and 3.9% (n=10) reported feeling uncomfortable (P<.001). In the adjusted models, much greater comfort with technology was significantly correlated with reduced odds of requesting High-Touch support (comfortable: adjusted odds ratio 0.118, 95% CI 0.055-0.255 and neutral: adjusted odds ratio 0.212, 95% CI 0.077-0.587), but internet access was not significantly correlated. Conclusions: The strongest predictor for support preference for digital enrollment among the participants was their comfort with technology rather than access alone. These findings illustrate the significance of participant-centric design methods coupling adaptive support paths, mixed enrollment strategies, and individualized onboarding methods aligned with digital confidence to promote equitable engagement in precision health research.
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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 | 0.005 | 0.007 |
| 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 | 0.001 |
| 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".