A Digital Health Evaluation Framework
Bibliographic record
Abstract
Purpose: Digital health evaluation frameworks are needed to guide the development, implementation and evaluation of innovative, integrated systems of digital care. In this paper we describe a framework that was developed to evaluate the implementation of digital health technologies at a regional level. Materials and Methods: The framework was developed in a series of iterative phases beginning with a review of digital health frameworks used in Canada, which was followed by a scoping review focused on frameworks, models and theories used internationally to evaluate digital health technologies. Data extracted from articles were analyzed thematically to arrive at factors, concepts and measures that were incorporated in the final version of the framework following researcher discussions. Results: A range of themes and concepts emerged in the areas of: (1) organizational and context factors, (2) system, (3) use and process, and (4) outcomes. Conclusions: Several new themes and concepts were identified and incorporated into the new digital health evaluation framework.
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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 | 0.279 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.023 | 0.011 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".