A parent–science partnership to improve postsurgical pain management in young children: Co-development and usability testing of the Achy Penguin smartphone-based app
Bibliographic record
Abstract
BACKGROUND: Young children are at risk for poorly managed pain after surgery, with significant negative consequence to their quality of life and health outcomes. Mobile applications offer a highly accessible, engaging, and interactive medium to improve pain assessment and management; however, they generally lack scientific foundation or support. AIMS: The aims of this study were to describe a successful parent-science partnership in the development and testing of Achy Penguin, a parent-developed iOS app to help assess and manage acute pain in young children, and to evaluate and refine the usability of Achy Penguin in young children with acute postoperative pain. METHODS: = 6-7 children/cycle). Semistructured qualitative interviews were analyzed using simple content analysis. RESULTS: Feedback from children and further integration of evidence-based pediatric pain knowledge led to refinements in app pain assessment and management content, as well as app flow and functionality. Changes improved children's ease of use and understanding and satisfaction by simplifying language in app instructions and content, adding audio and pictorial instructions, and increasing the engagement, interactiveness, immersiveness, and general appeal of pain management strategies. CONCLUSIONS: This article showcases the value of collaborative partnerships between various stakeholders (parents, app developers, and researcher/health care providers) to address gaps in pediatric pain care. The Achy Penguin app shows promise for improving pain assessment and management in young children, although further evaluation of app effectiveness and implementation is warranted.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".