Supporting parent capacity to manage pain in young children with cancer at home: Co‐design and usability testing of the PainCaRe app
Bibliographic record
Abstract
Young children receiving outpatient cancer care are vulnerable to undermanaged pain. App-based solutions that provide pain treatment advice to parents in real-time and in all environments may improve access to quality pain care. We used a parent co-design approach involving iterative rounds of user testing and software modification to develop a usable Pain Caregiver Resource (PainCaRe) real-time pediatric cancer pain management app. Parents of children (2-11 years) with cancer completed three standardized modules using a PainCaRe prototype. App usability and acceptability were evaluated using the validated System Usability Scale and a thematic analysis of app testing sessions and interviews. Iterative testing sessions were conducted until data saturation. Interview themes were synthesized into action items that guided revisions to PainCaRe and additional testing rounds were conducted as necessary. Twenty-two parents participated in three testing cycles. Overall, parents described PainCaRe as an acceptable and potentially clinically useful pain management tool. Mean system usability scores were in the acceptable scale range during each testing cycle. Usability issues identified and resolved included those related to software malfunction, complicated app navigation logic, lack of clarity on pain assessment questions, and the need for pain management advice specifically tailored to child developmental stage. Using co-design methods, the PainCaRe cancer pain management app was successfully refined for its acceptability and utility to parents. Next steps will include a PainCaRe pilot study before evaluating the impact of the app on younger children's pain outcomes in a randomized controlled trial.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".