The Relationship Between Insufficient Vision and Technology Access and Use Among Hospitalized Adults at an Urban Academic Hospital: Observational Study
Notice bibliographique
Résumé
BACKGROUND: The role of sufficient vision in self-management is salient with respect to the growing prevalence of eHealth-based interventions for chronic diseases. However, the relationship between insufficient vision and self-management has been understudied. OBJECTIVE: We aimed to assess differences in access to and use of technology among adults with and without insufficient vision at an academic urban hospital. METHODS: This is an observational study of hospitalized adult general medicine patients that is part of a larger quality improvement study called the hospitalist study. The hospitalist study provided demographic and health literacy data (Brief Health Literacy Screen). Our substudy included several measures. Validated surveys assessed technology access and use, and included benchmarked questions from the National Pew Survey to determine access to, willingness to use, and self-described ability to use technology at home, particularly for self-management, and eHealth-specific questions assessing future willingness to access eHealth post discharge. The eHealth Literacy Scale (eHEALS) was used to assess eHealth literacy. Visual acuity was assessed using the Snellen pocket eye chart with low vision defined as visual acuity ≤20/50 in at least one eye. Descriptive statistics, bivariate chi-square analyses, and multivariate logistic regressions (adjusted for age, race, gender, education level, and eHealth literacy) were performed using Stata. RESULTS: A total of 59 participants completed our substudy. The mean age was 54 (SD 16.4) years. Demographic data from the hospitalist study was missing for several participants. Among those who responded, most identified as Black (n=34, 79%) and female (n=26, 57%), and most reported at least some college education (n=30, 67%). Most participants owned technology devices (n=57, 97%) and had previously used the internet (n=52, 86%), with no significant differences between those with insufficient and sufficient vision (n=34 vs n=25). Though there was a 2x effect size for laptop ownership, with those with sufficient vision more likely to own a laptop, those with insufficient vision versus sufficient vision were less likely to report an ability to perform online tasks without assistance, including using a search engine (n=22, 65% vs n=23, 92%; P=.02), opening an attachment (n=17, 50% vs n=22, 88%; P=.002), and using an online video (n=20, 59% vs n=22, 88%; P=.01). In multivariate analysis, the ability to independently open an online attachment did not remain statistically significant (P=.01). CONCLUSIONS: Technology device ownership and internet use rates are high in this population, yet participants with insufficient vision (vs sufficient vision) reported a reduced ability to independently perform online tasks. To ensure the effective use of eHealth technologies by at-risk populations, the relationship between vision and technology use needs to be further studied.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».