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Record W4410911342 · doi:10.2196/63717

Capturing Community Perspectives in a Statewide Cancer Needs Assessment: Online Focus Group Study

2025· article· en· W4410911342 on OpenAlexvenueno aff
Jessica R. Thompson, Keeghan Francis, Caree R. McAfee, Madeline Brown, Todd Burus, Melinda Rogers, Connie Sorrell, Elizabeth Westbrook, Lovoria B. Williams, Jennifer Redmond Knight, Elaine Russell, Natalie P. Wilhite, Pamela C. Hull

Bibliographic record

VenueJMIR Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsPreprintFocus groupFocus (optics)PsychologySociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Kentucky has the highest all-site cancer incidence and mortality rates in the United States. Conducting needs assessments in a large geographic area, such as an entire state, poses challenges in collecting qualitative data from diverse rural and urban contexts. In 2021, a steering committee was formed to drive a multimethod, statewide cancer needs assessment (CNA) to identify the future priorities for all cancer-related care in Kentucky. OBJECTIVE: We aimed to report on the online focus group component of the CNA by documenting existing community resources and perceived needs across the cancer care continuum. In addition, we aimed to explore the impacts of social determinants of health among populations experiencing health disparities. METHODS: Through existing partnerships and a national research registry, we recruited adult Kentucky residents who were not employed in health occupations to participate in 11 online 60-minute focus groups, stratified to include multiple target populations and geographic areas. We based our semistructured discussion guide on the cancer care continuum and focused on social determinants of health, health equity, and factors affecting cancer diagnoses and outcomes. We conducted a qualitative line-by-line analysis of the recorded transcripts to identify themes. RESULTS: The participants (N=51; mean 4.63, SD 2.26 per group) lived in 25 different counties, including 35% (18/51) from rural communities, 14% (7/51) from the Appalachian area of Kentucky, and 31% (16/51) who self-identified with a racial or ethnic minority group. We identified 17 primary themes representing community-perceived needs and potential solutions across the cancer care continuum, including novel approaches to make information accessible; messaging not interpreted as blaming or shaming; messaging from individuals who engender trust; screening efforts to reach individuals where they are; ways to address practical barriers to screening and treatment, such as cost and transportation; and ways to increase knowledge about insurance coverage. In addition, we found 83 emergent subthemes specific to race, ethnicity, rural and urban residence, sexual orientation and gender identity, and age. The participants described the need to promote positive, culturally sensitive patient-health care provider communication and to create safe care spaces that consider the ways in which social norms affect cancer care, fight stigma, and improve health equity. CONCLUSIONS: By conducting statewide qualitative data collection online, we provided valuable depth of understanding for future programs and research to address cancer incidence and mortality in Kentucky. The findings pointed to several potential actions to address community-perceived needs across the cancer care continuum, including increasing accessible risk reduction information, expanding ways to overcome challenges to screening and treatment, building patient navigation resources, and increasing positive patient-health care provider communication. The findings also suggest that online focus groups can be a valuable component of CNAs to capture cancer-related needs and solutions across large geographic areas and diverse populations.

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 score0.101
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.455
Teacher spread0.356 · 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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