Developing an Evidence-Based Evaluation Framework for mHealth Applications
Bibliographic record
Abstract
The rapid growth of mobile health (mHealth) applications underscores the pressing need for robust evaluation platforms that ensure quality, efficacy, and stakeholder alignment. This paper introduces a quantitative, evidence-based framework that addresses these gaps through dynamic attribute weighting and multi-criteria decision analysis. The platform integrates methodologies such as CRITIC-TOPSIS and AI-driven attribute prioritization, validated through systematic reviews and expert analyses. Preliminary evaluations demonstrate potential for generating actionable insights tailored to diverse mHealth applications and stakeholder needs. Future work includes detailed literature reviews, platform development and real-life use case deployment to refine its applicability and impact.
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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 | 0.414 | 0.393 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.040 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| 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".