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Record W4413511760 · doi:10.2196/69097

Developing and Testing an Online Portal for Virtual Navigation for Asian American Patients With Cancer: Pilot Feasibility Study

2025· article· en· W4413511760 on OpenAlexvenueno aff
Janet N. Chu, Debora L. Oh, Laura Allen, Janice Y. Tsoh, Katarina Wang, Mei‐Chin Kuo, Hoan N. Bui, Andrea Hwang, Carmen Ma, Angeline Truong, Feng-Ming Li, Tung T. Nguyen, Scarlett Lin Gomez, Salma Shariff‐Marco

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Center for Chronic Disease Prevention and Health PromotionNational Cancer Institute
KeywordsPreprintAsian americansComputer scienceWorld Wide WebPsychologyPolitical science

Abstract

fetched live from OpenAlex

Background: Asian American patients have reported unique needs and barriers related to cancer care. While patient navigation can facilitate care coordination and help address barriers to care, in-person navigation is time and resource intensive. Virtual patient navigation can extend the benefits of patient navigation to more patients, especially those with non-English language needs. Objective: This study aimed to develop, implement, and test an online portal providing virtual navigation, including access to resources in English, Chinese, and Vietnamese, for Asian American patients with newly diagnosed colorectal, lung, or liver cancer. Methods: The online portal was built on a secure, cloud-based platform. We recruited adults aged 21 years or older with a recent diagnosis of stage I-IV colorectal, lung, or liver cancer; who identified as Asian American; spoke English, Cantonese, Mandarin, or Vietnamese; and resided in the Greater San Francisco Bay Area, California. Participants were assigned a language-concordant navigator who assessed their needs and provided tailored resources and support over 6 months through the online portal. Participants completed baseline, 3-month, 6-month, and user experience surveys. We report descriptive statistics on sociodemographic characteristics, quality of life (Functional Assessment of Cancer Therapy-General [FACT-G]), and user experiences. We used generalized estimating equations (GEE) to analyze repeated measures of quality of life. Results: The online portal included (1) a public-facing landing page, (2) a navigator interface, and (3) a participant interface, which were all available in English, Chinese, and Vietnamese. Among 51 participants, 47 (92%) and 49 (96%) completed the 3- and 6-month surveys, respectively. The mean age was 58 (SD 13) years, with 37 (73%) men, 33 (65%) speaking English, and 20 (39%) having less than a college education. Twenty-six participants (51%) had colorectal cancer, 21 (41%) had lung cancer, and 4 (8%) had liver cancer. The average total FACT-G score was 73.0 (SD 17) at baseline, 73.2 (SD 17) at 3 months, and 75.1 (SD 19) at 6 months. In GEE models, participants reported an increase in emotional well-being at 6 months compared to baseline (coefficient 0.99, 95% CI 0.01-1.97). Among the 47 participants who completed the user experience survey, some reported issues with registering and logging into the portal, but 44 (94%) reported that the program was culturally appropriate, 35 (74%) found calls from the navigators helpful, and 35 (74%) would recommend the program to others. Conclusions: This multilingual virtual patient navigation program for Asian American patients with cancer was deemed culturally appropriate and helpful in our pilot study. Emotional well-being improved among users of the portal. Some participants reported technical challenges, but most were satisfied with the program. Language-concordant virtual patient navigation and online supportive care tools can extend the reach and benefits of patient navigation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.391
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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