Implementing Patient Navigation for Children With CKD
Bibliographic record
Abstract
Introduction: Children with chronic kidney disease (CKD), particularly those who experienced socioeconomic disadvantage, have poorer health and lower quality of life (QoL), partly because of limited access to high-quality care. Patient navigation may improve access to care, self-advocacy, self-management, and emotional well-being. Methods: We conducted a national stakeholder workshop involving 38 participants (4 patients, 1 policy-maker/funder, 22 researchers, and 11 health care professionals) from 7 states or territories in Australia and discussed potential strategies to implement navigation programs in diverse CKD clinical settings. Results: We identified 7 key themes or strategies for implementation. "Securing sustainable funding" was considered necessary for program longevity. Prioritizing the "well-being of navigators" involved protecting their mental health. "Embedding patient navigation within the existing health care system" involved integrating navigators into the multidisciplinary care team. "Encourage robust communication through collaboration" empowered patients and families to make informed, shared decisions. "Targeting the appropriate population and situations" was emphasized to support patients and families during the most difficult phases of their CKD journey. Participants recognized the program's benefits, including improving care delivery fragmentation and "Adapting the model of care" to ensure appropriateness for diverse populations and settings. Conclusion: The navigation program can be adopted, adapted, and scaled-up for implementation to improve care coordination and access to quality care for children with CKD. Key strategies include securing long-term funding, supporting navigator well-being through training and peer support, integrating navigation into health care systems, and maintaining flexibility while preserving core elements.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".