‘Nothing about us Without us’ – Development of a Patient- centered Digital Health Self-care Program for Marginalized, Underserved Populations with Heart Failure
Notice bibliographique
Résumé
Heart failure (HF) is a global pandemic affecting over 26 million people worldwide. In high-income-countries (HICs), the use of evidence-based self-care models has led to a decline in HF prevalence. However, these improvements are less evident in low-and-middle-income countries (LMICs) and marginalized subgroup populations in HICs, like the Indigenous People, due to factors related to poor disease control and the disparities in a population’s social determinants of health. With the inequitable distribution of health services, digital health has been offered as an avenue to assist populations with limited resources. While mobile phones have become increasingly utilized, many digital health interventions have failed to be adopted, as they have been designed for usage in high-resource settings. With the current mismatch between technological innovation and local community priorities, this research sought to 1) evaluate the state of heart health within the remote communities in Northern Ontario and Northern Uganda, 2) investigate the contextual requirements to design of a community-based digital health program, and 3) adapt the program according to the identified design requirements. In Study 1a, we established that Indigenous communities and LMICs valued the use of digital tools, but its adoption would be dependent on its cultural compatibility. To better understand how cultural context should be integrated within digital tool design, Study 1b explored how various community engagement strategies could be utilized. Using these findings, in Study 1c, a research partnership was established with each community. In Study 2, a community-based needs assessment was conducted to evaluate the contextual influencers impacting community heart health. In Study 3, we developed a series of design requirements focused on empowering existing community resources and cultural values. In a society where the distribution of wealth is heavily unbalanced, there is concern that the digital divide will compound the effects of socioeconomic divisions. While the COVID-19 pandemic triggered the momentum for digital health, populations with a history of being overlooked, continue to be left with minimal support. As such, to close the gap associated with the digital divide, interventions need to be designed in reflection of the contextual circumstances contributing to a population’s poorer health outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».