Strategies for Tailoring Patient-Centered Technologies Across the Cancer Continuum: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: In the United States, cancer is more prevalent in racial and ethnic minority groups and in rural-dwelling and low-income people. Compared with White people of non-Hispanic descent, Black and African American people have higher cancer mortality and Hispanic people are more likely to be diagnosed with infection-related cancers. In addition, people who live in persistent poverty areas are more vulnerable to cancer mortality. Tailoring health information technologies (HITs) can help bridge health inequities by providing these populations with relevant health information and cancer care. Cultural tailoring in health care involves adapting interventions to reflect a population's values, history, and attitudes that influence behavior. OBJECTIVE: The goals of the current study are as follows: 1) to understand what elements of tailoring HITs are most effective among different underserved populations, 2) to identify ways of incorporating these elements to improve the acceptability and effectiveness of technology-based interventions, and 3) to develop a framework to tailor HITs to underserved populations and improve engagement and acceptability. METHODS: A scoping review will explore how HITs have been culturally tailored to underserved populations using PubMed, Scopus, and Web of Science database searches. Our search strategy will include terms and medical subject headings associated with the categories of cancer, HITs, tailoring, and underserved populations. We will also perform a snowball search of the references of included studies. We will include quantitative and qualitative peer-reviewed, English-language studies from the United States that examine efforts to tailor HIT interventions to improve their acceptance, use, and usability among underserved populations. Predefined inclusion and exclusion criteria will be applied for study selection. For each included study, we will extract the following data: study design, cancer type, underserved population of interest, details of the technology used, study methods, sample size, study outcomes, user acceptability, and tailoring and targeting strategies. The data will be summarized descriptively and analyzed thematically. RESULTS: Preliminary searches following this strategy yielded a total of 784 citations (after removing duplicates) that will each be reviewed by at least 2 reviewers for inclusion. This protocol was submitted before data collection. The search strategy, citation screening, and data extraction will commence in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and the published protocol. Findings will be expected by the spring of 2026. CONCLUSIONS: There is a need to develop more accessible HITs for underserved populations. This scoping review will inform researchers, providers, and developers working on cancer-specific HITs for underserved populations, such as racial and ethnic minority groups, rural-dwelling residents, and low-income populations. By summarizing evidence on tailoring strategies by population and delivery mode, the review aims to support the development of more effective and acceptable technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73705.
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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 | 0.102 | 0.117 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.017 |
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".