030 Evidence based & lived-experience informed: co- designing an intervention to increase shared decision- making for children with medical complexity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".