Principles and Framework for eHealth Strategy Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Significant investment in eHealth solutions is being made in nearly every country of the world. How do we know that these investments and the foregone opportunity costs are the correct ones? Absent, poor, or vague eHealth strategy is a significant barrier to effective investment in, and implementation of, sustainable eHealth solutions and establishment of an eHealth favorable policy environment. Strategy is the driving force, the first essential ingredient, that can place countries in charge of their own eHealth destiny and inform them of the policy necessary to achieve it. In the last 2 years, there has been renewed interest in eHealth strategy from the World Health Organization (WHO), International Telecommunications Union (ITU), Pan American Health Organization (PAHO), the African Union, and the Commonwealth; yet overall, the literature lacks clear guidance to inform countries why and how to develop their own complementary but locally specific eHealth strategy. To address this gap, this paper further develops an eHealth Strategy Development Framework, basing it upon a conceptual framework and relevant theories of strategy and complex system analysis available from the literature. We present here the rationale, theories, and final eHealth strategy development framework by which a systematic and methodical approach can be applied by institutions, subnational regions, and countries to create holistic, needs- and evidence-based, and defensible eHealth strategy and to ensure wise investment in eHealth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it