Patient Navigation: Core Concepts and Relevance to the Field of Pediatric Neurodisability
Bibliographic record
Abstract
The concept of patient navigation emerged nearly three decades ago, and has been applied and examined within myriad health and social service contexts. In this chapter, we consider the specific case of children with neurodevelopmental disorders and disabilities (NDD/D) and their families, as this population experiences significant challenge in connecting to supports and services and in accessing continuous care. We suggest that the core tenets of “navigation,” as described by Dr. Harold Freeman and others, lend themselves well to this circumstance, and have great potential to improve the ways in which these children and families access resources across care sectors, domains, and levels. In what follows, we present an overview of the evolution of patient navigation as an area of practice and research, outlining the core attributes guiding such activities, as well as diversity in application. We then turn to the context of NDD/D, reviewing the limited work that has been done, and consider how we can apply lessons from other health contexts to this unique circumstance. We argue that consideration of patient navigation for children with NDD/D and their families is sorely needed and share suggestions as to how we might begin to move this field forward.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".