An Intelligent Customer-Driven Digital Solution to Improve Perioperative Health Outcomes Among Children Undergoing Circumcision and Their Parents: Development and Evaluation
Bibliographic record
Abstract
BACKGROUND: Circumcision as a common elective pediatric surgery worldwide is a stressful and anxiety-inducing experience for parents and children. Although current perioperative interventions proved effective, such as reducing preoperative anxiety, there are limited holistic solutions using mobile apps. OBJECTIVE: This paper aims to describe the development and primary evaluation of an intelligent customer-driven smartphone-based app program (ICory-Circumcision) to enhance health outcomes among children undergoing circumcision and their family caregivers. METHODS: Based on the review of the literature and previous studies, Bandura's self-efficacy theory was adopted as the conceptual framework. A multidisciplinary team was built to identify the content and develop the apps. Semistructured interviews were conducted to evaluate the ICory-Circumcision. RESULTS: The ICory-Circumcision study was carried out from March 2019 to January 2020 and comprised 2 mobile apps, BuddyCare app and Triumf Health mobile game app. The former provides a day-by-day perioperative guide for parents whose children are undergoing circumcision, while the latter provides emotional support and distraction to children. In total, 6 participants were recruited to use the apps and interviewed to evaluate the program. In total, 4 main categories and 10 subcategories were generated from content analysis. CONCLUSIONS: ICory-Circumcision seemed to lean toward being useful. Revisions to ICory-Circumcision are necessary to enhance its contents and features before advancing to the randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04174404; https://clinicaltrials.gov/ct2/show/NCT04174404.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".