Examining the Impact of Selected Sociodemographic Factors and Cancer-Related Fatalistic Beliefs on Patient Engagement via Health Information Technology Among Older Adults: Cross-Sectional Analysis
Notice bibliographique
Résumé
BACKGROUND: Despite the role of health information technology (HIT) in patient engagement processes and government incentives for HIT development, research regarding HIT is lacking among older adults with a high burden of chronic diseases such as cancer. This study examines the role of selected sociodemographic factors and cancer-related fatalistic beliefs on patient engagement expressed through HIT use for patient engagement in adults aged ≥65 years. We controlled for cancer diagnosis to account for its potential influence on patient engagement. OBJECTIVE: This study has 2 aims: to investigate the role of sociodemographic factors such as race, education, poverty index, and psychosocial factors of cancer fatalistic beliefs in accessing and using HIT in older adults and to examine the association between access and use of HIT in the self-management domain of patient activation that serves as a precursor to patient engagement. METHODS: This is a secondary data analysis of a subset of the Health Information National Trend Survey (Health Information National Trend Survey 4, cycle 3). The subset included individuals aged ≥65 years with and without a cancer diagnosis. The relationships between access to and use of HIT to several sociodemographic variables and psychosocial factors of fatalistic beliefs were analyzed. Logistic and linear regression models were fit to study these associations. RESULTS: This study included 180 individuals aged ≥65 years with a cancer diagnosis and 398 without a diagnosis. This analysis indicated that having less than a college education level (P=<.001), being an individual from an ethnic and minority group (P=<.001), and living in poverty (P=.001) were significantly associated with decreased access to HIT. Reduced HIT use was associated with less than a college education (P=.001) and poverty(P=.02). This analysis also indicated that fatalistic beliefs about cancer were significantly associated with lower HIT use (P=.03). Specifically, a 1-point increase in the cancer fatalistic belief score was associated with a 36% decrease in HIT use. We found that controlling for cancer diagnosis did not affect the outcomes for sociodemographic variables or fatalistic beliefs about cancer. However, patients with access to HIT had a self-management domain of patient activation (SMD) score of 0.21 points higher (P=.003) compared with patients who did not have access. SMD score was higher by 0.28 points (P=.002) for individuals who used HIT and 0.14 points higher (P=.04) who had a prior diagnosis of cancer. CONCLUSIONS: Sociodemographic factors (education, race, poverty, and cancer fatalistic beliefs) impact HIT access and use in older adults, regardless of prior cancer diagnosis. Among older adults, HIT users report higher self-management, which is essential for patient activation and engagement.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».