Drivers that decrease hospital-delivered care in children with medical complexity: Parental perspectives
Bibliographic record
Abstract
Background and objective: Children with medical complexity (CMC) have chronic and severe conditions leading to medical fragility. CMC represent less than 1% of children but account for one-third of paediatric healthcare expenditures. Enrollment to a complex care program (CCP) decreases health care resource utilization while improving parental satisfaction. An in-depth understanding of how these changes operate in real-world setting is needed to further support CMC and their families. This study aimed at assessing the possible reasons for a decrease in emergency department (ED) visits and hospitalization length of stay related to enrollment to a CCP, based on parental perspectives. Study design: Using a qualitative approach, data were collected using in-depth, semi-structured interviews with parents of CMC enrolled in a CCP from a university hospital centre in Montreal, Canada. The interview guide was co-constructed by an interdisciplinary team, including a parent partner and a clinical nurse coordinator. Themes have been identified inductively, using thematic analysis. Results: as enablers arising from the CCP that contributed to the decrease in hospital-delivered care utilization. Improvement in medical baseline condition was also identified as a contributing factor, while not necessarily related to program's support. Conclusions: In this study, we identified personalized care, parental empowerment, and guidance as three strategies for a CCP to potentially decrease ED visits and hospital length of stay, from the parents' perspective. Parents identified the clinical nurse coordinator as playing a central role in supporting the implementation of these strategies.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".