Development of a Conceptual Framework for e-Health Readiness Assessment in the Context of Developing Countries
Bibliographic record
Abstract
Background: e-Health readiness has been described as the preparedness of healthcare institutions, communities, or individuals for the anticipated change brought by programmes related to ICT use. Assessment of e-health readiness prior to the implementation of e-health innovations can therefore facilitate the process of change for individuals and organisations to adopt e-health programmes and avoid disappointment. The literature shows that although many e-health readiness assessment frameworks and tools exist, none meet all the requirements for e-health readiness assessment in developing countries. The aim of this study was to develop an e-health readiness assessment framework applicable to developing countries. Methods: A three-step process gleaned from the e-health literature (literature review / material collection; analysis / content analysis; consolidation / conceptual framework synthesis), together with iterative and reflective processes based on prior research undertaken by this group, guided framework conceptualisation and design. Results: An evidence-based framework was developed that: incorporates the need to assess readiness for each e-health component separately; identifies government’s central role in engaging all relevant stakeholders; and the need to assess the adequacy of a country’s infrastructure and infostructure prior to e-health planning and possible implementation. Also addressed by the framework is a need for an e-health readiness assessment to be undertaken using separate tools for technical and non-technical individuals. A country’s e-Readiness is highlighted as an important indicator for e-health readiness. Conclusions: The intent of the final framework is to inform and assist policy and decision makers, and facilitate future successful implementation of e-health initiatives in the developing world.
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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 | 0.046 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".