Impact of a community-based pediatric oncology nursing model
Bibliographic record
Abstract
20645 Background: The care of pediatric cancer patients is complex, involving multiple providers. Despite high cure rates, the care experience is known to place significant supportive care burden on patients and family members. New and evolving models of nursing care have been developed to address the complex needs of of patients and help address the perceived gaps in coordination of care but to date, there are few reports of outcomes. In this study we examined the impact of a community based specialist oncology nursing program as reported from the perspective of members of pediatric cancer care teams in Ontario, Canada. Methods: Qualitative case study using semi structured interviews with oncology teams in the 5 regional pediatric cancer programs in Ontario, Canada. Interview guides were devloped using literature on impacts of nursing interventions, expert opinion and data from a previous study in adult populations to determine role, overlap, barriers and impacts on patients and the care system. Interviews were transcribed and coded by 2 independant investigators using a pre specified coding schema. The constant comparative method was used to indentify themes not covered by the coding template. Data triangulation was used to confirm findings and member checking was used to validate responses. Results: 20 interviews were completed with oncologists (n=6), case managers (n=4), hospital nurses (n=5) and social workers (n=5) across the 5 regional centres. Respondants consistently reported that the program has had postitive impacts on meeting supportive care need of patients and families, that care coordination had improved and that utilization of hospital services had decreased. Particularly notable was the establishment of consistent palliative care and school/social integration for survivors. Oncologists noted improvements in workload specifially around supportive care and infromational support. No role conflict was reported by hospital nurses but social workers did suggest some role overlap with the nursing program. Conclusions: These results support the positive impact of the specialist nursing program in the care of pediatic cancer patients in Ontario, Canada from the perspective of care providers. Future research is planned to study impact from the perspective of patients and family members. No significant financial relationships to disclose.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".