COG KidsCare App Improves Caregivers’ Perceived Education and Support During Their Child's Cancer Journey
Bibliographic record
Abstract
Background: The Children's Oncology Group (COG) KidsCare App (App) is an mHealth tool that contains educational materials for pediatric oncology caregivers. While the usability of the App has been studied, the effects of the App on the experience of caregivers have not been examined. The objective of this study was to evaluate the impact of the App on education and support of pediatric oncology caregivers. Method: A 23-item multiple-choice questionnaire was developed using Opinio software and disseminated via hyperlink embedded in the App and on paper. Parents/caregivers participated in a virtual, semi-structured focus group hosted by a parent/caregiver. Participants were identified by nurse investigators using convenience sampling. Data were analyzed using descriptive statistics. Results: Twenty-five caregivers completed the questionnaire; six participated in interviews. The App provided valuable educational materials for 88% of respondents; 60% agreed it helped them feel better supported. All participants in the focus groups unanimously agreed that the App would have been helpful at the beginning of their treatment. Discussion: Results demonstrate that the COG KidsCare App improves caregivers’ education and positively impacts their perception of support. It is likely most impactful for families who are within 6 months of a cancer diagnosis and should be provided to families as early as possible. Revisiting the App with families throughout their treatment journey and into follow-up is also recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".