TOwards enhancing Paediatric Intensive Care for Children with Medical Complexity (ToPIC CMC): a mixed-methods study protocol using Experience-based Co-design
Bibliographic record
Abstract
INTRODUCTION: Advances in medical technology and postoperative care have led to increased survival of children with medical complexity (CMC). Parents of CMC develop substantial caregiver expertise and familiarity with paediatric intensive care unit (PICU) staff and treatment procedures which may give rise to tensions regarding respective roles, caretaking preferences, treatment goals and expected outcomes. A therapeutic alliance built through strong partnerships constitutes the foundation of patient and family-centred care (PFCC), contributing to improvements in experiences and outcomes. Yet acute care settings continue to struggle with integrating PFCC into practice. This study aims to enhance PFCC for CMC in the PICU using an innovative approach to integrated knowledge translation. METHODS: A mixed-method concurrent triangulation design will be used to develop, implement and evaluate PFCC practice changes for CMC in the PICU. Qualitative data will be collected using an Experience-based Co-design (EBCD) approach. Parents, CMC and staff will reflect on their PICU care experiences (stages 1 and 2), identify priorities for improvement (stage 3), devise strategies to implement changes (stage 4), evaluate practice changes and study process, and disseminate findings (stage 5). The quantitative arm will consist of a prepractice and postpractice change evaluation, compared with a control site. Analysis of qualitative and quantitative data will provide insights regarding the impact of PICU practice changes on PFCC. ETHICS AND DISSEMINATION: The McGill University Health Centre Research Ethics Board (Ref. #2019-5021) and the Hospital for Sick Children Research Ethics Board (Ref. #1000063801) approved the study. Knowledge users and researchers will be engaged as partners throughout the study as per our participatory approach. Knowledge products will include a short film featuring themes and video/audio clips from the interviews, recommendations for improvements in care, and presentations for healthcare leaders and clinical teams, in addition to traditional academic outputs such as conference presentations and publications.
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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 | 0.100 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".