Characteristics of Acute Childhood Illness Apps for Parents: Environmental Scan
Bibliographic record
Abstract
BACKGROUND: Providing parents with resources that aid in the identification and management of acute childhood illnesses helps those parents feel better equipped to assess their children's health and significantly changes parental health-seeking behaviors. Some of these resources are limited by accessibility and scalability. Remote locations and staffing limitations create challenges for parents aiming to access their child's health information. Mobile health apps offer a scalable, accessible solution for improving health literacy by enabling access to health information through mobile devices. OBJECTIVE: The aim of our study is to create an inventory of acute childhood illness apps that are available to North American parents and caregivers, assess their quality, and identify the areas in which future apps can be improved. METHODS: We conducted an environmental scan to identify and summarize app information for parents and digital health researchers. The Google and Apple app marketplaces were used as search platforms. We built a list of search terms and searched the platforms for apps targeted at parents and related to acute pediatric illnesses in the United States and Canada. We assessed apps meeting the inclusion criteria using the Mobile App Rating Scale (MARS), a validated tool for assessing the quality of health apps. The MARS examines apps on 5 subscales: engagement, functionality, aesthetics, information quality, and subjective quality. Data were analyzed by MARS subscale averages and individual item scores. RESULTS: Overall, 650 unique apps were screened, and 53 (8.2%) were included. On a scale of 1-5, apps had an average engagement score of 2.82/5 (SD 0.86), functionality score of 3.98/5 (SD 0.72), aesthetics score of 3.09/5 (SD 0.87), information quality score of 2.73/5 (SD 1.32), and subjective quality score of 2.20/5 (SD 0.79). On the same scale of 1-5, app scores ranged from 2.2/5 to 4.5/5 (mean 3.2, SD 0.6). The top 3 MARS-scored apps were Baby and Child First Aid (4.5/5), Ada (4.5/5), and HANDi Paediatric (4.2/5). Taken together, the top 3 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; this space was filled with content originating primarily in the United Kingdom and the United States. In addition, published evidence of the impact of the included apps was poor: of 53 apps, only 5 (9%) had an evidence base showing that the app had been trialed for usability or efficacy. CONCLUSIONS: There is a need for evidence-based acute childhood illness apps of Canadian origin. This environmental scan offers a comprehensive picture of the health app landscape by examining trends in acute childhood illness apps that are readily available to parents and by identifying gaps in app design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".