Refinement of Health App Review Tool (HART) through stakeholder interviews: HART 2.0
Bibliographic record
Abstract
The Health App Review Tool (HART) is a novel assessment designed to match users with Alzheimer's disease or related dementias (ADRD) and caregivers to mobile applications that support health and wellness. The objectives of this study were to gather stakeholder feedback on the HART and then to implement revisions. Thirteen participants completed in-depth Think Aloud interviews. Participants shared qualitative feedback on each HART item. Participant feedback was analyzed via in-depth video-audio review. Feedback was implemented as actionable HART revisions. On average, the majority of participants rated items as "adequate"; however, qualitative findings indicated the need for improvement in conciseness, clarity, and understandability. Conciseness was addressed by combining related concepts into multi-items, clarity through the addition of specific examples, and understandability through improved verbiage. The HART has been refined from 106 items to 17 items through extensive revisions to the clarity, conciseness, and explanations provided throughout the assessment.
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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 | 0.187 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".