In‐hospital usability and feasibility evaluation of Panda, an app for the management of pain in children at home
Bibliographic record
Abstract
BACKGROUND: Postoperative pain in children is often poorly managed at home, leading to slower functional recovery, poor oral intake, sleep disturbances, and behavioral changes. Panda is a smartphone application (app) designed to support parents in assessing their child's pain and managing medications. AIMS: The aim of this study was to evaluate the Panda app's usability and feasibility in hospital prior to testing the app at home. METHODS: The study comprised two phases. Phase I evaluated Panda's usability with nurses, parents, and adolescents using simulated scenarios. Usability was measured by task completion rate, user error rates, and the Computer Systems Usability Questionnaire. Phase II evaluated Panda's feasibility by observing parents/guardians of pediatric patients using the app on the postsurgical ward. Feasibility was measured using response frequency and delay following app notifications from an audit trail of app function, and parental satisfaction from an interview. Feedback was used to guide iterative app improvements. RESULTS: In Phase I, 13 nurses, 12 parents, and 5 adolescents evaluated the app. A total of 103 usability issues were identified, analyzed, and addressed. In Phase II, 29 parents responded to a total of 151 app notifications, with 84% responding within 1 hour in the final round of testing; 93% of participants reported the app was easy to use, and rated the app with a median [interquartile range] Computer Systems Usability Questionnaire score of 2 [1-4]. Significant barriers to use included lack of flexibility in the medication scheduling, low volume of alert sounds, and the extra time spent on medication safety checks. CONCLUSION: Panda's usability was improved and its feasibility demonstrated in the controlled hospital environment. The next step is to evaluate its feasibility for use at home.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".