Increasing Colorectal Cancer Screening Among Black Men in Virginia: Development of an mHealth Intervention
Bibliographic record
Abstract
BACKGROUND: In the United States, colorectal cancer (CRC) is the third leading cause of cancer death among Black men. Compared to men of all other races or ethnicities, Black men have the lowest rates of CRC screening participation, which contributes to later-stage diagnoses and greater mortality. Despite CRC screening being a critical component of early detection and increased survival, few interventions have been tailored for Black men. OBJECTIVE: This study aims to report on the multistep process used to translate formative research including prior experiences implementing a national CRC education program, community advisory, and preliminary survey results into a culturally tailored mobile health (mHealth) intervention. METHODS: A theoretically and empirically informed translational science public health intervention was developed using the Behavioral Design Thinking approach. Data to inform how content should be tailored were collected from the empirical literature and a community advisory board of Black men (n=7) and reinforced by the preliminary results of 98 survey respondents. RESULTS: A community advisory board identified changes for delivery that were private, self-paced, and easily accessible and content that addressed medical mistrust, access delays for referrals and appointments, lack of local information, misinformation, and the role of families. Empirical literature and survey results identified the need for local health clinic involvement as critical to screening uptake, leading to a partnership with local Federally Qualified Health Centers to connect participants directly to clinical care. Men surveyed (n=98) who live or work in the study area were an average of 59 (SD 7.9) years old and held high levels of mistrust of health care institutions. In the last 12 months, 25% (24/98) of them did not see a doctor and 16.3% (16/98) of them did not have a regular doctor. Regarding CRC, 27% (26/98) and 38% (37/98) of them had never had a colonoscopy or blood stool test, respectively. CONCLUSIONS: Working with a third-party developer, a prototype mHealth app that is downloadable, optimized for iPhone and Android users, and uses familiar sharing, video, and text messaging modalities was created. Guided by our results, we created 4 short videos (1:30-2 min) including a survivor vignette, animated videos about CRC and the type of screening tests, and a message from a community clinic partner. Men also receive tailored feedback and direct navigation to local Federally Qualified Health Center partners including via school-based family clinics. These content and delivery elements of the mHealth intervention were the direct result of the multipronged, theoretically informed approach to translate an existing but generalized CRC knowledge-based intervention into a digital, self-paced, tailored intervention with links to local community clinics. TRIAL REGISTRATION: ClinicalTrials.gov NCT05980182; https://clinicaltrials.gov/study/NCT05980182.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".