Next Step, Adulthood: The Role of Nurse Navigators in Supporting the Transition of Neurodivergent Youth from Pediatric to Adult Health Services in Canada
Bibliographic record
Abstract
In Canada, 7% of youth 15-24 identify as living with neurodevelopmental conditions or intellectual disabilities, such as autism spectrum disorder or attention deficit disorder. These youth face significant challenges when transitioning from pediatric to adult healthcare services. Fragmented systems, diminished support, and increased health vulnerabilities mark these transitions. Adult healthcare systems often lack the coordinated, family-centered approach of pediatric care, resulting in care gaps, mental health risks, and poor health outcomes. This commentary argues that nurse navigators offer a practical, evidence-informed, and ethically grounded solution to improve transitional care. Drawing on successful models in oncology care, the nurse navigator role can bridge service silos, advocate for inclusive care, and support youth and families during this critical life stage. Despite systemic barriers such as workforce shortages and inconsistent policy implementation, the integration of nurse navigators into Canadian healthcare frameworks represents a necessary step toward equitable, person-centered transitional care. This paper calls for national investment in a scalable navigation model tailored to the needs of neurodivergent youth and their families, which aligns with Canada's legislative commitments to accessibility, inclusion, and health equity.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.027 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".