96 The pediatric health app landscape: Results of an environmental scan
Bibliographic record
Abstract
Abstract Primary Subject area Technology Background Providing resources that aid in detection and identification of serious childhood illness helps parents feel better equipped to assess their child’s health and significantly changes parental health-seeking behaviours. Some of these resources are limited by accessibility and scalability. Remote locations and staffing limitations create challenges for parents aiming to access health information about their child. Mobile health (mHealth) apps offer a scalable, accessible solution for improving health literacy by enabling access to health information through mobile devices. Objectives To systematically identify all Canadian and US child health apps for parents. Design/Methods We used an environmental scan approach – a review designed to summarize information for decision makers. The Android and Apple app marketplaces were used as search platforms. We built a list of search terms and used these platforms to search for apps targeted at parents, related to pediatric illnesses in US and Canada. Then once we determined that the app met the inclusion criteria, each included app was assessed using the Mobile App Rating Scale (MARS), a validated tool for assessing health apps. The MARS examines apps on five domains: engagement, functionality, aesthetics, information quality, and subjective quality. Results 650 unique apps were screened and 53 apps were included. On a scale of 1-5, the 53 apps assessed had an average Engagement score of 2.82/5 (θ = 0.86), Functionality of 3.98/5 (θ = 0.72), Aesthetics of 3.09/5 (θ = 0.87), Information quality of 2.73/5 (θ = 1.32), and Subjective quality of 2.2 (θ = 0.79). App scores ranged from 2.2/5 to 4.5/5 (µ= 3.2, θ =0.6).The top three apps were Baby and Child First Aid (4.5/5), Ada (4.5/5), and HANDi Paediatric (4.2/5). Taken together, the top three apps covered topics of emergency pediatric first aid, identification of (and appropriate response to) common childhood illnesses, a means of checking symptoms, and a means of responding to emergency situations. There was a lack of Canadian-based app content available to parents in both marketplaces. In addition, published evidence of the included apps’ impact was poor: only 5/53 apps had an evidence base showing the app had been trialed. Conclusion There is a need for evidence-based apps of Canadian origin related to childhood illnesses. The results of this scan created a picture of the health app landscape by examining trends in apps related to pediatric health that are readily available to parents, and by identifying gaps in app design that can be addressed.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".