Apps for Promoting Children’s Oral Health: Systematic Search in App Stores and Quality Evaluation
Bibliographic record
Abstract
BACKGROUND: Increasingly, mobile apps are being used to promote oral care. Many of them are aimed at children. OBJECTIVE: This study aimed to systematically search and evaluate apps that promote oral care and hygiene for children. METHODS: A broad search strategy (13 keywords) was developed to identify apps from Apple's App Store and the Google Play Store in April 2019. After reviewing the apps' titles and summaries, potentially relevant apps were downloaded for viewing. The quality of the apps that met the inclusion criteria was assessed by the Health on the Net Foundation Code of Conduct (HONcode) criteria for medical and health websites and the Scientific Basis of Oral Self-care (SBOSC). RESULTS: More than 3000 Apps were identified and 54 relevant apps informed the review. The quality of the apps according to the HONcode criteria was generally low. The mean HONcode score was 1.8/8.0. One-quarter of the apps had a HONcode score of 0 (14/54, 26%). The SBOSC score of the apps was evaluated based on a 6-point scale. The mean SBOSC score was 1.5/6.0; 19% (10/54) of the apps had a score of 0. There was a significant and positive correlation between HONcode and SBOSC scores (r=0.37; P<.01). More recently uploaded apps had significantly higher HONcode scores (P<.05). CONCLUSIONS: There are many apps aiming to promote oral self-care among children. The quality and scientific basis of these apps are low. Newer apps are of higher quality in terms of scientific basis. There is a need to ensure high-quality and evidence-based apps are available. The effectiveness of apps in terms of oral care and clinical outcomes among children needs to be evaluated.
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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 | 0.049 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.046 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".