Capturing Community Perspectives in a Statewide Cancer Needs Assessment: Online Focus Group Study
Notice bibliographique
Résumé
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.
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 | 0,019 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».