Engagement With a Health Information Technology–Augmented Self-Management Support Program in a Population With Limited English Proficiency: Observational Study
Notice bibliographique
Résumé
BACKGROUND: Limited English proficiency (LEP) is an important driver of health disparities. Many successful patient-level interventions to prevent chronic disease progression and complications have used automated telephone self-management support, which relies on patient activation and communication to achieve improved health outcomes. It is not clear whether these interventions are similarly applicable to patients with LEP compared to patients with English proficiency. OBJECTIVE: The objectives of this study were as follows: (1) To examine the impact of LEP on patient engagement (primary outcome) with a 12-month language-concordant self-management program that included automated telephone self-management support, designed for patients with chronic kidney disease (CKD). (2) To assess the impact of LEP on change in systolic blood pressure (SBP) and albuminuria (secondary outcomes) resulting from the self-management program. METHODS: This was a secondary analysis of the Kidney Awareness Registry and Education (KARE) pilot trial (NCT01530958) which was funded by the National Institutes of Health in August 2011, approved by the University of California Institutional Review Board in October 2011 (No. 11-07399), and executed between 2013 and 2015. Multivariable logistic and linear models were used to examine various facets of patient engagement with the CKD self-management support program by LEP status. Patient engagement was defined by patient's use of educational materials, completion of a health coaching action plan, and degree of participation with automated telephone self-management support. Changes in SBP and albuminuria at 12 months by LEP status were determined using multivariable linear mixed models. RESULTS: Of 137 study participants, 53 (38.7%) reported LEP, of which 45 (85%) were Spanish speaking and 8 (15%) Cantonese speaking. While patients with LEP and English proficiency similarly used the program's educational materials (85% [17/20] vs 88% [30/34], P=.69) and completed an action plan (81% [22/27] vs 74% [35/47], P=.49), those with LEP engaged more with the automated telephone self-management support component. Average call completion was 66% among patients with LEP compared with 57% among those with English proficiency; patients with LEP requested more health coach telephone calls (P=.08) and had a significantly longer average automated call duration (3.3 [SD 1.4] min vs 2.2 [1.1 min], P<.001), indicating higher patient engagement. Patients with LEP randomized to self-management support had a larger, though nonstatistically significant (P=.74), change in SBP (-4.5 mmHg; 95% CI -9.4 to 0.3) and albuminuria (-72.4 mg/dL; 95% CI -208.9 to 64.1) compared with patients with English proficiency randomized to self-management support (-2.1 mmHg; 95% CI -8.6 to 4.3 and -11.1 mg/dL; 95% CI -166.9 to 144.7). CONCLUSIONS: Patients with LEP with CKD were equally or more engaged with a language-concordant, culturally appropriate telehealth intervention compared with their English-speaking counterparts. Augmented telehealth may be useful in mitigating communication barriers among patients with LEP. TRIAL REGISTRATION: ClinicalTrials.gov NCT01530958; https://clinicaltrials.gov/ct2/show/NCT01530958.
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».