Using American Sign Language–Fluent Community Health Navigators to Advance Cancer Screening Adherence through Videoconferencing With Deaf, Deafblind, and Hard of Hearing Adults: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Cancer screening nonadherence persists among adults who are deaf, deafblind, and hard of hearing (DDBHH). These barriers span individual, clinician, and health care system levels, contributing to difficulties understanding cancer information, accessing screening services, and following treatment directives. Critical communication barriers include ineffective patient-physician communication, limited access to American Sign Language (ASL) cancer information, misconceptions about medical procedures, insurance navigation difficulties, and intersectional barriers for multiply marginalized individuals. OBJECTIVE: This randomized controlled trial addresses these barriers by implementing the first videoconference-based study of ASL-fluent community health navigators (ASL-CHNs) to improve cancer screening adherence among adults who are DDBHH. The study tests whether ASL-CHN intervention results in greater adherence to cancer screening guidelines, improved patient-physician communication ratings, and increased cancer knowledge compared to standard care. METHODS: The study uses a videoconference-delivered, block-randomized design stratifying 200 participants who are DDBHH by age and sex, with 100 participants assigned to the ASL-CHN intervention and 100 to standard care. All participants are confirmed as nonadherent to at least 1 of 5 age-appropriate cancer screening guidelines recommended by the United States Preventive Services Task Force for breast, cervical, colorectal, lung, and prostate cancers. Recruitment occurred nationwide through multiple strategies including prior study participants, community partners, and major community events. The intervention arm receives support from specially trained ASL-CHNs over several months, accommodating lengthy scheduling processes for cancer screenings. Primary outcomes measure completion of age- and risk-appropriate cancer screening, with prostate cancer focusing on shared decision-making participation. Secondary outcomes assess patient-physician communication using the validated National Cancer Institute's Health Information National Trends Survey (NCI-HINTS) Patient Centered Communication questionnaire in ASL. Tertiary outcomes examine cancer knowledge through validated measures. The analysis uses intent-to-treat methodology using multivariable logistic regression, accounting for potential clustering effects and anticipated 25% attrition. RESULTS: As of August 2025, more than 75% of the target enrollment has been achieved. Preliminary data indicate that the intervention group is consistently outperforming the standard care group in cancer screening adherence, supporting the study hypothesis that ASL-CHNs are effective in promoting cancer screening adherence among previously nonadherent participants who are DDBHH. CONCLUSIONS: The ASL-CHN intervention represents an accessible, scalable solution for reducing cancer screening disparities. By combining personalized navigation with ASL-fluent community health support through videoconferencing, this intervention addresses limitations of previous screening programs that lacked accessible support. If successful, the ASL-CHN model could provide health care providers with a practical, recommendable option for patients who are DDBHH requiring navigator support that can be done remotely through videoconferencing, potentially improving early detection rates and reducing cancer mortality in this underserved population while advancing accessible care delivery. TRIAL REGISTRATION: ClinicalTrials.gov NCT06492993; https://clinicaltrials.gov/study/NCT06492993. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65078.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".