030 Evidence based & lived-experience informed: co- designing an intervention to increase shared decision- making for children with medical complexity
Notice bibliographique
Résumé
Introduction Children with medical complexity (CMC) are the most medically fragile sub-set of paediatric patients who require intensive support from caregivers.1 CMC are mostly cared for on general pediatric inpatient units (GPIU) where hospitalizations are acute, frequent and often prolonged with an increased risk for adverse outcomes.2–4 During hospitalizations, due to the co-existence of underlying diseases, caregivers and clinicians of CMC often face decisions with unclear answers, with inadequate evidence to support treatment options. To overcome these challenges, studies have called for the development of evidence-based interventions tailored to the perceived and/or experienced barriers and facilitators of SDM.5 Methods Employing a co-design methodology, with an integrated pragmatic evaluation component6 enabled caregivers and health care professionals caring for CMC to co-design an intervention to facilitate better SDM while hospitalized. In a 5-hour co-design workshop, guided by design thinking methodology,7 participants empathized with those engaging in SDM through role plays, identified key areas of tension, collaboratively generated ‘blue sky’ interventions to address the identified tensions, with two groups each prototyping one identified solution using summarised evidence on SDM. Results Collaboration, humility, roles, skills and knowledge were all identified by participants as the key areas of tension or uncertainty in SDM encounters. The two interventions prototyped,1 a caregiver extension to an electronic medical record, and2 a multi-modal training program with supplemental tools, apps, podcasts and evaluations, complemented each other and were deemed practical, robust, implementable and sustainable by participants including administrators responsible for implementation. Discussion/Conclusion Two interventions were co-designed and prototyped to address means of clarifying, making explicit and practicing the components of SDM identified in the model, while promoting feelings of partnership, trust and benevolence. Further research is required to further refine the prototypes further explore implementation and testing. References Cohen E, Kuo DZ, Agrawal R, Berry JG, Bhagat SKM, Simon TD, et al. Children with medical complexity: an emerging population for clinical and research initiatives. Pediatrics. 2011;127(3):529–38. Berry JG, Hall DE, Kuo DZ, Cohen E, Agrawal R, Feudtner C, et al. Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals. JAMA. 2011;305(7):682–90. Gold JM, Hall M, Shah SS, Thomson J, Subramony A, Mahant S, et al. Long length of hospital stay in children with medical complexity. J Hosp Med. 2016;11(11):750–6. Simon TD, Mahant S, Cohen E. Pediatric hospital medicine and children with medical complexity: past, present, and future. Curr Probl Pediatr Adolesc Health Care. 2012;42(5):113–9. Boland L, Graham ID, Légaré F, Lewis K, Jull J, Shephard A, et al. Barriers and facilitators of pediatric shared decision-making: a systematic review. Implement Sci. 2019;14(1):7. Feldstein AC, Glasgow RE. A practical, robust implementation and sustainability model (PRISM) for integrating research findings into practice. Jt Comm J Qual Patient Saf. 2008;34(4):228–43. Ku B, Lupton E. Health design thinking: creating products and services for better health. mit Press; 2022.
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 | 0,027 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
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.
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 ».