Process Evaluation of a Hub-and-Spoke Model to Deliver Coordinated Care for Children with Medical Complexity across Ontario: Facilitators, Barriers and Lessons Learned
Bibliographic record
Abstract
BACKGROUND: Complex Care for Kids Ontario (CCKO) is a multi-year strategy aimed at expanding a hub-and-spoke model to deliver coordinated care for children with medical complexity (CMC) across Ontario. OBJECTIVE: This paper aims to identify the facilitators, barriers and lessons learned from the implementation of the Ontario CCKO strategy. METHOD: Alongside an outcome evaluation of the CCKO strategy, we conducted a process evaluation to understand the implementation context, process and mechanisms. Semi-structured interviews were conducted with 38 healthcare leaders, clinicians and support staff from four regions involved in CCKO care delivery and/or governance. RESULTS: Facilitators to CCKO implementation were sustained engagement of system-wide stakeholders, inter-organizational partnerships, knowledge sharing and family engagement. Barriers to CCKO implementation were resources and funding, fragmentation of care, aligning perspectives between providers and clinical staff recruitment and retention. CONCLUSION: A flexible approach is required to implement a complex, multi-centre policy strategy. Other jurisdictions considering such a model of care delivery would benefit from attention to contextual variations in implementation setting, building cross-sector engagement and buy-in, and offering continuous support for modifications to the intervention as and when required.
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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 | 0.085 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".