Conceptual Framework for Development of Comprehensive e-Health Evaluation Tool
Bibliographic record
Abstract
OBJECTIVE: The main objective of this study was to develop an e-health evaluation tool based on a conceptual framework including relevant theories for evaluating use of technology in health programs. This article presents the development of an evaluation framework for e-health programs. MATERIALS AND METHODS: The study was divided into three stages: Stage 1 involved a detailed literature search of different theories and concepts on evaluation of e-health, Stage 2 plotted e-health theories to identify relevant themes, and Stage 3 developed a matrix of evaluation themes and stages of e-health programs. RESULTS: The framework identifies and defines different stages of e-health programs and then applies evaluation theories to each of these stages for development of the evaluation tool. This framework builds on existing theories of health and technology evaluation and presents a conceptual framework for developing an e-health evaluation tool to examine and measure different factors that play a definite role in the success of e-health programs. The framework on the horizontal axis divides e-health into different stages of program implementation, while the vertical axis identifies different themes and areas of consideration for e-health evaluation. CONCLUSIONS: The framework helps understand various aspects of e-health programs and their impact that require evaluation at different stages of the life cycle. The study led to the development of a new and comprehensive e-health evaluation tool, named the Khoja-Durrani-Scott Framework for e-Health Evaluation.
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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 | 0.083 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".