Health App Review Tool (HART): Content validation through expert panel review
Bibliographic record
Abstract
The Health App Review Tool (HART) is an evaluation tool that is designed to help the users in evaluation of the health apps for Alzheimer's Disease and Related Dementias (ADRD) population. As the development of the HART continues, the domain items that HART addresses require evaluation to determine if they meet the intended required criteria for the users.To complete content validation of the HART 10 health care professions provided content validation of the HART via a content validation form. Specifically, data collection took place virtually through Microsoft Teams and Qualtrics-based content validity index. Following, revisions were made through a consensus process involving 3 rehabilitation experts, minimizing potential conflicts.Findings indicate 76 of 109 items were considered acceptable, 19 items were in need of review and 14 items in need of revision. In sum 30% of the total HART items required either review or revision to improve HART validity. The changes were implemented through consensus revisions.
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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 | 0.362 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".