Strategies for Tailoring Patient-Centered Technologies Across the Cancer Continuum: Protocol for a Scoping Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,102 | 0,117 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,006 |
| Méta-épidémiologie (sens large) | 0,012 | 0,016 |
| Bibliométrie | 0,021 | 0,020 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,009 | 0,011 |
| Science ouverte | 0,007 | 0,011 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,085 | 0,017 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».