Canadian Pediatric Oncology Nurses’ Perspectives on Implementation of the Children's Oncology Group KidsCare Customized App
Bibliographic record
Abstract
Background: Canada represents half of the Children's Oncology Group (COG) sites that have opted to customize content for families within the COG KidsCare app. It was unclear how many sites proceeded with developing and inputting customized content and how well the app and customized content were implemented into practice. This raised concerns that Canadian families were unaware of this new digital resource and did not have equitable access to customized content. This qualitative study aimed to understand nursing site leads’ experiences including perspectives on facilitators and barriers to customization and implementation of the COG's KidsCare app. Method: Semi-structured interviews with clinicians who self-reported expertise in patient and family education local practices were conducted. Transcripts were independently coded by two team members using an iterative hybrid inductive/deductive approach, and analyzed using the Consolidated Framework for Implementation Research (CFIR), to summarize results. Results: The facilitators and barriers to implementing the COG KidsCare app with customization were categorized by five overarching CFIR-related themes: (a) features of the customized COG KidsCare app, (b) external environment, (c) institutional environment, (d) implementation team, and (e) the process of implementing and customizing the COG KidsCare app. Discussion: Nurses expressed feelings of tension between support and perceived value of the COG KidsCare app with customization, and their ability to successfully create, refine, implement content and disseminate to families. Using our RoadMap of recommended implementation strategies to integrate use of the app into practice may provide opportunity for successful implementation in a variety of contexts.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".