The Development of a Digital Patient Navigation Tool to Increase Colorectal Cancer Screening Among Federally Qualified Health Center Patients: Acceptability and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Federally Qualified Health Centers (FQHCs) are an essential place for historically underserved patients to access health care, including screening for colorectal cancer (CRC), one of the leading causes of cancer death in the United States. Novel interventions aimed at increasing CRC screening completion rates at FQHCs are crucial. OBJECTIVE: This study conducts user testing of a digital patient navigation tool, called eNav, designed to support FQHC patients in preparing for, requesting, and completing CRC screening tests. METHODS: We recruited English- and Spanish-speaking patients (N=20) at an FQHC in New York City to user-test the eNav website (2 user tests; n=10 participants per user test). In each user test, participants engaged in a "think aloud" exercise and a qualitative interview to summarize and review their feedback. They also completed a baseline questionnaire gathering data about demographics, technology and internet use, medical history, and health literacy, and completed surveys to assess the website's acceptability and usability. Based on participant feedback from the first user test, we modified the eNav website for a second round of testing. Then, feedback from the second user test was used to modify and finalize the eNav website. RESULTS: Survey results supported the overall usability and acceptability of the website. The average System Usability Scale score for our first user test was 75.25; for the second, it was 75.28. The average Acceptability E-scale score for our first user test was 28.3; for the second, it was 29.2. These scores meet suggested benchmarks for usability and acceptability. During qualitative think-aloud exercises, in both user tests, many participants favorably perceived the website as motivating, interesting, informative, and user-friendly. Respondents also gave suggestions on how to improve the website's content, usability, accessibility, and appeal. We found that some participants did not have the digital devices or internet access needed to interact with the eNav website at home. CONCLUSIONS: Based on participant feedback on the eNav website and reported limitations to digital access across both user tests, we made modifications to the content and design of the website. We also designed alternative methods of engagement with eNav to increase the tool's usability, accessibility, and impact for patients with diverse needs, including those with limited access to devices or the internet at home. Next, we will test the eNav intervention in a randomized controlled trial to evaluate the efficacy of the eNav website for improving CRC screening uptake among patients treated at FQHCs.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".