Post Intensive care outcomes and follow-up in Children: A Collaboration of Health care providers, researchers, and families Utilizing knowledge co-production
Bibliographic record
Abstract
Abstract Background Many children do not return to their pre-admission health status following admission to the paediatric intensive care unit (PICU), facing a range of physical, cognitive, emotional, and social challenges collectively known as Post-Intensive Care Syndrome in Paediatrics (PICS-p). The sequelae associated with PICS-p necessitate comprehensive follow-up care intending to address these multifaceted needs. The P ost I ntensive care outcomes and follow-up in C hildren: A C ollaboration of H ealth care providers, researchers, and families U tilizing knowledge co-production (PICACHU) study aims to develop a shared care follow-up service for post-PICU patients and their families. It also seeks to facilitate outcomes research and identify quality improvement (QI) initiatives to mitigate the impact of PICS-p. Methods The study employs a pragmatic approach informed by the Medical Research Council (MRC) framework and co-design methodology. The research includes surveys and focus group discussions (FGDs) with purposively sampled post-PICU families, acute care pediatricians, community pediatricians, general practitioners (GPs), and primary care nurse practitioners (NPs). Data collection tools include adapted versions of existing surveys and semi-structured interview guides. The analysis will involve qualitative and quantitative methods, utilising SPSS for statistical analysis and NVivo for thematic analysis of FGDs. Conclusion The PICACHU study is the first of its kind to use a co-design approach to create a post-PICU shared care follow-up service in British Columbia (BC), Canada. The findings will provide valuable insights for improving post-PICU care services in BC and potentially other jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".