Integrated care networks in multidisciplinary rehabilitation therapy services for childhood oncology close to home: lessons learned from an international environmental scan
Bibliographic record
Abstract
BACKGROUND: Integrated care networks (ICNs) close to home have the potential to improve continuity and quality of care for children with cancer and their families during and after treatment. Our goal is to develop such a network for multidisciplinary rehabilitation therapy services (RTS) in The Netherlands, but we lacked a good understanding of an ICN and the factors to make it successful. PURPOSE: The aim of the study was to learn from initiatives in ICN's internationally, how are ICN's developed, how does it promote collaboration and what are facilitators and barriers in its development and use? METHODS: We performed an environmental scan. First, we performed a systematic literature search (PubMed) focussing on ICNs for childhood oncology. Secondly, we sent a survey regarding development and use of ICNs to international childhood cancer centers. Participating centers were asked to share information about their initiatives in providing care close to home. Data were summarized descriptively and analyzed using content analysis. RESULTS: The literature search did not reveal any relevant publications. The results from the survey, including15 countries, provided valuable insights in the understanding of a good ICN, its facilitators and barriers, and the potential added value of developing ICNs close to home to provide continuity and quality of care. CONCLUSIONS: Our study highlights the perceived importance of ICNs for multidisciplinary RTS in pediatric oncology and provides valuable information for the formation of such a network. Information about the needs from the perspectives of children and parents is currently missing and essential to develop successful ICNs.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".