Paediatric patient navigation models of care in Canada: An environmental scan
Bibliographic record
Abstract
OBJECTIVES: (1) To provide other organizations with useful information when implementing paediatric navigation programs and (2) to inform the implementation of a navigation care centre in New Brunswick for children with complex health conditions. METHODS: This environmental scan consisted of a literature review of published and grey literature for paediatric patient navigation programs across Canada. Additional programs were found following discussions with program coordinators and navigators. Interviews were conducted with key staff from each program and included questions related to patient condition; target population and location; method delivery; navigator background; and navigator roles. Data analysis included analysis of interviews and identification of common themes across the different programs. RESULTS: We interviewed staff from 19 paediatric navigation programs across Canada. Programs varied across a number of different themes, including: condition and disease type, program location (e.g., hospital or clinic), navigator background (e.g., registered nurse or peer/lay navigator) and method of delivery (e.g., phone or face-to-face). Overall, navigator roles are similar across all programs, including advocacy, education, support and assistance in accessing resources from both within and outside the health care system. DISCUSSION: This scan offers a road map of Canadian paediatric navigation programs. Knowledge learned from this scan will inform stakeholders who are either involved in the delivery of paediatric patient navigation programs or planning to implement such a program. Specifically, our scan informed the development of a navigation centre for children with complex health conditions in New Brunswick.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.027 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".