Caregiver and Youth Characteristics That Influence Trust in Digital Health Platforms in Pediatric Care: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Combining patient-generated health data and digital health platforms may improve patient experience and population health, mitigate rising health care costs, reduce clinician burnout, and enable health equity. However, lack of trust may be a notable barrier to the data-sharing required by such platforms. Understanding sociodemographic, health, and personal characteristics will enable developers and implementers of such technologies to consider these in their technical design requirements. OBJECTIVE: This study aims to understand relationships between sociodemographic characteristics of caregivers of children or adolescents and trust in and willingness to use digital platforms to store and share personal health information for clinical care and research. METHODS: This study used a mixed methods approach, including surveys of caregivers of youth aged <18 years living in Canada or the United States and youth aged 16 to 17 years living in Canada, as well as web-based bulletin board discussions to further explore topics of trust in data sharing. Sociodemographic and survey data were tabulated and explored using proportional odds ordinal regression models. Comments from web-based group discussions were analyzed thematically using a coding approach to identify issues important to the participants. RESULTS: Survey data from 1128 caregivers (female participants: n=549, 48.7%; 36-50 years old: n=660, 58.5%; Canadian: n=603, 53.5%; urban population: n=494, 43.8%) were collected, of which 685 (60.7%) completed all questions. Data from 173 youth (female participants: n=73, 42.2%; urban population: n=94, 54.3%) were collected, of which 129 (74.6%) completed all questions, and data were available for analysis. Furthermore, among 40 participants, 23 (58%) caregivers contributed to the web-based discussion boards. Related to trust, living in a rural area (vs urban; odds ratio [OR] 0.66, 95% CI 0.46-0.95) resulted in lower concern for data privacy and security, while having an undergraduate (OR 1.82, 95% CI 1.30-2.55) or graduate degree (vs secondary or trade school; OR 2.50, 95% CI 1.68-3.73) resulted in higher levels of concern. Living with a chronic disease (OR 1.81, 95% CI 1.35-2.44) increased levels of concern regarding data privacy and security. Interestingly, those with chronic disease were more willing to use digital platforms for clinical care and share personal health information for not-for-profit research. Caregivers were most concerned about data breaches involving data from their children but also highlighted that digital platforms would allow for better coordination of care for their children. CONCLUSIONS: Our research confirms the willingness of caregivers and youth to use digital platforms for both clinical care delivery and research and suggests that the value of a digital platform may outweigh the risks of its use. Engagement of end users in co-designing such platforms has the potential to enhance digital trust. However, digital trust varies across sociodemographic groups; therefore, diverse end user engagement is necessary when designing digital applications.
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 | 0,011 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».