Do eHealth literacy and socio-demographics predict patients' preferences for use of eHealth programmes after percutaneous coronary intervention?
Notice bibliographique
Résumé
Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): The Western Norway Health Authority Background Evidence supports the use of electronic health (eHealth) programmes for patients with coronary artery disease. To date, there has been little attention toward patients’ preferences for the use of eHealth programmes, and their associations with eHealth literacy and socio-demographic factors post percutaneous coronary intervention (PCI). Purpose To determine how eHealth literacy and socio-demographics are associated with patients’ preferences for use of eHealth programmes assessed 12-month post PCI. Methods An observational cohort study recruited 3417 adult patients treated by PCI at three Norwegian and four Danish university hospitals, June 2017-May 2019. Socio-demographic data and self-reported outcomes on eHealth literacy (eHealth literacy scale) were assessed at baseline. De novo questions on preferences for use of eHealth programmes were collected 12-month post PCI. Hierarchical logistic regression models were performed. Results The majority of patients were men (78%), and the mean age was 66 years. Almost 40% were interested in participating in eHealth programmes. The odds of being interested in accessing a webpage with quality ensured information, health applications and online chat function with healthcare providers increased with 2-3% for each point higher eHealth literacy score, which indicates better eHealth literacy. After controlling for age, education and gender (Step 2), eHealth literacy no longer remained a significant predictor for patients’ preferences. Males had 49% higher odds for interest in a webpage with quality ensured information than females. Females had 33-34% higher odds for interest in an online chat function with healthcare providers and an individually tailored text message. The odds for interest in a webpage with quality ensured information, health applications, online chat function with healthcare providers and individually tailored text messages, decreased with 2-5% per year higher age. For individual tailored feedback on email, the odds for interes was 1% higher per year higher age. Educational level above primary school was a robust predictor for the interest in a webpage with quality ensured information (63-240%). Compared to those with primary school education level, those with college/university education had 67% higher odds for interest in short online information videos, 123% higher odds for interest in online chat function with healthcare providers and 61% higher odds for interest in individually tailored feedback on email compared to patients’ whit primary school. Patients with high school and college/university education had 34-57% lower odds for interest in individually tailored text messages than those with primary school. Conclusions Age, educational level and gender were important predictors of patients’ preferences for using eHealth programmes post PCI. These results are important for the further development of personalized eHealth programmes.
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 | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».