Multiple stakeholders’ perspectives on improving virtual cancer care for older Black adults
Notice bibliographique
Résumé
Introduction: Cancer is the leading cause of death in Canada. Social distancing regulations during the COVID-19 pandemic drove the rapid transitions of health care systems from in-person to virtual cancer care. Virtual care refers to the secure usage of various forms of information and communication technologies such as phone calls, video conferencing, or patient portals to facilitate/optimize the quality of patient care. Virtual care has been shown to be effective for a variety of uses in cancer care, such as pre-treatment discussions and counselling purposes. However, recent research suggests that although virtual cancer care (VCC) can enhance access to health services for some communities, it can also exacerbate health inequities for socially disadvantaged groups such as Racialized communities that may not be able to afford necessary digital technology required for virtual care. Furthermore, older adults face additional challenges when interacting with technology including restricted ability to use technology due to physical or cognitive limitations. Although Black people in Canada are reported to have the lowest rates of access to digital health technologies, it is still unclear how race affects VCC in Canada. It is essential that VCC is delivered in a manner that meets the needs of all communities and does not exacerbate inequities between the socially advantaged and socially disadvantaged communities. The objective of this study was to better understand the VCC experience of older Black patients, their caregivers and healthcare providers. Methods: Qualitative studies are appropriate to capture the lived experiences of individuals/communities of a social phenomena such as VCC. We used a theory-informed thematic analysis, using data collected from six focus groups (N= 55 participants: 40 patients and caregivers, and 15 healthcare providers) conducted across ten Canadian provinces. Qualitative data analysis was facilitated by the Nvivo 11 software. Data was coded using the Patient Centred Care model and the Synergies of Oppression framework guided interpretation. Results: The experiences of older Black patients, their caregivers and healthcare providers in using VCC were captured in five overarching themes: Patient at the intersection of multiple systems of oppression; Shifting role of caregivers; Giving choice and choosing based on the purpose of care; Opportunity to meet health care needs through digital access; Communicating effectively through virtual care. Eight barriers to optimal VCC such as limited digital literacy, linguistic barriers in traditional African/Caribbean languages, and culturally mediated views of patients were identified. We also identified six facilitators to optimal VCC such as community-based cancer support groups, caregivers support and key features of digital technologies. Conclusion: This study suggests that efforts to improve the quality of VCC necessitates a multipronged approach that addresses barriers while leveraging culturally sensitive guides to VCC. Initiative to redesign VCC programs tailored to the needs of socially disadvantaged communities like older Black patients may improve the virtual care experience for the whole population. Public policies and practices that address issues like availability of internet in remote areas, resources to support linguistic barriers or culturally sensitive training are important in responding to the complexity of access to VCC
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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 | 0,015 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,023 | 0,009 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,002 | 0,014 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».