Bridging evidence-to-care gaps with mHealth: Designing a symptom checker for parents accessing knowledge translation resources on acute children’s illnesses in a smartphone application
Bibliographic record
Abstract
Background: Smartphone applications offer a novel platform for delivering health information to parents. This study created and evaluated an app-based symptom checker that recommends educational tools to parents based on their child's symptoms. Methods: Symptoms extracted from 23 knowledge translation (KT) tools for 10 children's illnesses comprised a set of plain-language symptoms. The symptom checker works by producing confusion matrices evaluating a child's reported symptoms against possible illnesses, comparing precision scores to examine how well each illness matched reported symptoms, and ordering possible illnesses by performance score. Performance was evaluated by extracting symptoms from 8 clinical vignettes, and examining correct first-try matches. Results: We created a final list of 54 plain-language symptoms. Visualizations of the symptom set creation process and logic mapping are presented, as well as images of the working symptom checker. The symptom checker matched 100% (8/8) of tested clinical vignettes to the appropriate illness resource. Discussion: Symptom checkers are a potentially useful tool to integrate into apps that parents use for their children's health. The design of these systems has the potential to change parents' relationship with technology, affecting both their adoption and acceptance of symptom checkers. Our design choices contribute to addressing current barriers to the adoption of symptom checkers, reducing functional, critical, and interactive literacy requirements for parents.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".