Children's Oncology Group <i>KidsCare</i> smartphone application for parents of children with cancer
Bibliographic record
Abstract
BACKGROUND: Parents of children with cancer must learn and retain crucial information necessary to provide safe care for their child. Smartphone applications (apps) provide a significant opportunity to meet the informational needs of these parents. We aimed to develop, refine, and evaluate a smartphone app, informed by the Children's Oncology Group (COG) expert consensus recommendations, to support the informational needs of parents of children with cancer. PROCEDURE: We employed a user-centered iterative mixed-methods approach in two phases (prototype development/refinement and pilot testing). We engaged parents and clinicians in evaluating the app via qualitative interviews and standardized tools that measured app quality (Mobile Application Rating Scale [MARS]), usability (System Usability Scale [SUS]), and acceptability (System Acceptability Scale [SAS]). We evaluated early usage patterns after public release. RESULTS: Thirty-two parents and 17 clinicians participated. Mean (± standard deviation [SD]) scores for app quality, usability, and acceptability were: MARS: 4.5 ± 0.7 on a 5-point scale; SUS: 86.7 ± 23.8 on a 100-point scale; and SAS: superior (61%); similar (28%); inferior (11%) to written materials. Qualitative findings largely confirmed the quantitative data. Downloads of the app during the first year following public release have exceeded 5000. CONCLUSIONS: The COG KidsCare app prototype was found to be of high quality and received high usability and acceptability ratings. Further testing is needed to determine app effectiveness in improving parental knowledge regarding care of children with cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".