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Enregistrement W4413358420 · doi:10.5334/ijic.nacic24078

From Planting Seeds to Driving Transformation - Patient/Family Advisors with Care Providers Co-designing Healthcare Improvement

2025· article· en· W4413358420 sur OpenAlexaffabout
Marian George, Katharina Kovacs Burns

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésHealth careBusinessNursingMedicinePolitical science

Résumé

récupéré en direct d'OpenAlex

Background: Transforming or making a difference in healthcare quality and safety requires active involvement of those affected in delivery as well as receiving care. Co-design is an opportunity for care providers to partner with patients/families and for patients/families to be more then recipients of care. However, understanding the barriers and challenges around co-design readiness for quality and safety improvement or sustained transformation is critical for effective implementation. The objective of our work was to () bring together key stakeholders of patients/families, care providers, and leaders in quality, safety and policy to explore and map out the essential components of co-design as related to quality and safety improvements in care; (2) co-develop a co-design approach that could be implemented in different care settings; and (3) evaluate the outcomes of the co-design process as well as resulting improvements in patient experiences with the quality and safety of their care. Approach: Different care settings across Alberta Health Services were approached (i.e. acute, home care, continuing care, long term care and others). Sites in agreement to apply co-design recruited staff, care providers, leaders, and patient/family advisors (PFAs) to be actively involved as care setting teams throughout the process. Each care setting team participated in identifying what they needed to understand the reality of quality and safety in their settings (e.g. existing patient safety and care experience data; safety issues, etc.), and what they felt they needed to implement their co-designed plans and activities with the intent of improving or transforming their quality and safety outcomes. Developmental evaluation was embedded throughout the co-design process to gather the experiences and perceptions of all stakeholders involved. Real-time patient/family experiences were gathered by PFAs via interviews/surveys pre and post quality/safety improvement initiatives for each care setting. Quality and safety metrics were reviewed at team meetings to inform any further actions needed. Results: The co-design approach was implemented and evaluated with 22 care settings, involving 69 staff/care providers and 7 patient/family advisors. As the co-design process unfolded in each care setting there was confirmation of what an appropriate co-design approach was. Experience data gathered from care setting team members determined that an orientation and four-phased co-design implementation approach for quality and safety improvement worked best. Experiences overall were positive and affirming for the co-design approach and outcomes achieved. Themes emerged for each of the four phases of the co-designed work - for example, "having clear direction for work planned/proposed" and "making a difference". Real-time patient/family experiences gathered also indicated improvements in various quality and safety areas - e.g. reduction in noise levels enhanced patients' much needed rest/sleep and reduced stress levels. Implication: Care setting teams learned to co-design, experienced the benefits of co-designing quality and safety improvement for their care settings, and found it feasible to sustain. They felt they made a difference in the overall experiences of patients and families. The resulting Kovacs Burns Geoge co-design orientation 0 guide for healthcare quality and safety improvement has been published and is available for teams to explore and apply.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,060
score de la tête « metaresearch » (Gemma)0,062
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,060
Score d'incertitude au seuil0,318

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0600,062
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0100,013
Communication savante0,0120,009
Science ouverte0,0030,013
Intégrité de la recherche0,0030,006
Charge utile insuffisante (le modèle a refusé de juger)0,0090,002

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.

Tête enseignante Opus0,040
Tête enseignante GPT0,376
Écart entre enseignants0,335 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

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