Principles and Framework for eHealth Strategy Development
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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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 | 0.054 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".