Patients and Care Providers Translating Care Transition, Integration, Coordination and Continuity Practices into Real Experiences
Notice bibliographique
Résumé
Background: The literature confirms the struggle healthcare service providers and recipients have with clearly describing perspectives, experiences, and outcomes of patients/families (P/F) and care providers (HCPs) regarding care transitions, and particularly as aligned with care integration, coordination and continuity. Most studies focused only on P/F experiences framed around patient or person-centred care. Most measures were developed by multidisciplinary teams including researchers and clinicians but void of P/Fs. Most did not capture parallel experiences of HCPs.How aligned or divergent are P/F and HCP care transition perceptions and experience with those regarding care integration, coordination, and continuity? Unless P/Fs and HCPs co-design their own care experience measures, how will we really understand what matters to them? Our study aimed to improve understanding of the P/F and HCP perspectives and experiences through: () co-designing with P/Fs and HCPs key experience domains and measures that mattered to them regarding successful care transitions including care integration, coordination and continuity practices and policies; (2) identifying what worked well and what needed improving to achieve successful patient transitions across settings; and (3) sharing findings to inform quality improvement strategies for care transitions with consideration for the pillars, principles, policies and practices of care integration, coordination and continuity. Approach: Between 208 and 2024, broad recruitment strategies were employed across acute and community-based settings in the five zones within Alberta Health Services with the intent of having multiple self-selected settings participate in building their capacity to co-design and implement a care transitions quality improvement strategy. Specifically, the request was for care settings to () establish a team of HCPs, quality improvement staff and P/F advisors; (2) learn to co-design P/F and HCP care transition experience measurement surveys/tools that included exploring integration, coordination and continuity of care; and (3) gather and analyze data to inform care transition practices, policies and system factors needing improvement generally, and specifically as aligned with integration, coordination and continuity. Results: Thirty-two care teams involving 79 staff and HCPs, and 26 P/F advisors agreed to participate in the study, co-designing relevant P/F and HCP care transition measurement tools that included some measures from the literature and what they felt mattered to them regarding integration, coordination, and continuity of care. P/F advisors gathered ,038 P/F survey responses and 40 care staff/HCPs completed online surveys. The aggregated findings guided the development of a set of care transition domains and measures, inclusive of relevant integration, coordination, and continuity care. Findings guiding our understanding of the experiences of P/Fs and HCPs regarding what worked well, and where there were issues, were also used to inform quality and safety improvement initiatives for each setting. Study findings were shared with key stakeholders and leaders. Implication: P/Fs and HCPs co-designing what matters to them regarding care transition perspectives and experiences enhanced our understanding of what and how these measures aligned with those regarding care integration, coordination, and continuity. Exploring and describing one of these care types includes consideration of the others. Together they more appropriately inform quality care and areas of improvement.
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 | 0,026 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,010 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 ».