Improving Care for Families and Children with Neurodevelopmental Disorders and Co-occurring Chronic Health Conditions Using a Care Coordination Intervention
Bibliographic record
Abstract
OBJECTIVE: This clinical intervention study aimed to improve care integration and health service delivery for children with concurrent neurodevelopmental disorders and chronic health conditions. This population has significant unmet needs and disproportionate deficits in service delivery. A lack of coordination across child service sectors is a common barrier to successful treatment and support of children with neurodevelopmental disorders with complex medical needs. METHODS: This project implemented an innovative care coordination model, involving one-on-one supports from a trained care coordinator who liaised with the broader intersectoral care team to improve joint care planning, integration of services, and the experience of both families and care providers. To evaluate the impact of care coordination activities, a single-group interventional study was conducted using a repeated-measures framework (at 0, 6, and 12 months) using previously established outcome measures. RESULTS: Over 2 years, this project provided care coordination to 84 children and their families, with an age range from 2 to 17 years. The care coordination intervention demonstrated positive impacts for children, families, and care teams and contributed to clinical efficiencies. Children had fewer visits to the emergency department and less frequent acute care use. Improvement in access to services, joint care planning and communication across providers, and better linkage with school supports were demonstrated. Families reported that the program decreased their stress around coordinating care for their child. CONCLUSION: This work demonstrated that intersectoral care coordination is attainable through innovative and collaborative practice for children with complex neurodevelopmental and medical needs.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".