Implementation and evaluation of a patient-focused eHealth intervention, My Kidneys My Health, in primary care and general nephrology clinics (Preprint)
Notice bibliographique
Résumé
BACKGROUND Care for mild to moderate chronic kidney disease (CKD) entails self-management from patients and clinical support from primary care and nephrology. In response to a gap in resources for this population, My Kidneys My Health was co-developed to support self-management of CKD. While this is a patient-facing tool, health care providers play a critical role in the implementation of patient resources OBJECTIVE This study develops and evaluates strategies to implement My Kidneys My Health into routine primary care and general nephrology clinical care. METHODS Health care providers working in Alberta, Canada who support patients with CKD were invited to participate in our multi-step study, guided by the Quality Implementation Framework. Step 1: we followed qualitative descriptive methodology to identify barriers and enablers to implementation using a directed content analysis and a deductive coding approach. Participants were invited to complete semi-structured interviews from October 2021 to May 2022. Step 2: we identified, prioritized, co-developed, and launched implementation strategies based on behaviour change theory. Participants from Step 1 were invited to use the materials during the implementation period (May to October 2022). Utilization was tracked through Google Analytics and document distribution tracking. Step 3: we conducted follow-up interviews with participants (October to December 2022) to evaluate implementation based on the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, following the same qualitative approach as Step 1. Effectiveness was out of scope of this study. RESULTS 14 health care providers participated in Step 1 qualitative interviews (42.9% from nephrology clinics, 35.7% from primary care). Participants shared an individual-level readiness and interest in sharing My Kidneys My Health with their patients. The key barriers to implementation included awareness, memory, time, motivation, and innovation accessibility. Implementation strategies were co-designed and implemented by Step 1 participants (i.e., educational sessions and materials, reminders, implementation coaching). 9 health care providers participated in Step 3 qualitative interviews. Participants shared their approach to tailoring implementation based on their patients and integrating the resource into their current practices. The resources developed were highly utilized by participants, with positive feedback on their usability. Participants expressed motivation to continue sharing My Kidneys My Health; however, awareness and accessibility require further adaptations can improve sustainability of implementation. Our rigorous approach allowed us to address behaviour change and sustainability of implementation of My Kidneys My Health, as well as identifying appropriate and tailored implementation strategies CONCLUSIONS There is a readiness to implement self-management supports for patients with early-stage CKD. A theory-informed approach and strategic implementation strategies can support sustainability. CLINICALTRIAL n/a INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1007/s43477-022-00038-3
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 | 0,028 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».