Bibliographic record
Abstract
The care of a child/adolescent with cancer or a blood disorder is complex and often long term, involving many interdisciplinary team members across services and geographical boundaries. This experience can be overwhelming for patients and their families, highlighting the need for a family care coordinator (FCC) to help them navigate their care path. The purpose of this article is to illustrate the concept of family care coordination as experienced by the IWK Health Center in Nova Scotia, Canada, with the intent of sharing a valuable model of care with other pediatric hematology/oncology services. Key components of the role are ongoing assessment, education, partnerships, communication, support, and advocacy. Essential resources and pathways are required to implement the role and optimize patient/family outcomes, facilitating consistent and accessible care, enhancing quality and safety, building trust, and gleaning efficiencies. Inherent FCC challenges are identified as time constraints, replacement issues, maintaining professional boundaries, and emotional burnout. A FCC can enable seamless, individualized care for children/adolescents and their families with pediatric oncological and hematological disorders, optimizing the outcomes for all involved.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.116 | 0.016 |
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".