e-Health Readiness Assessment Tools for Healthcare Institutions in Developing Countries
Bibliographic record
Abstract
e-Health Readiness refers to the preparedness of healthcare institutions or communities for the anticipated change brought by programs related to Information and Communications Technology (ICT). This paper presents e-Health Readiness assessment tools developed for healthcare institutions in developing countries. The objectives of the overall study were to develop e-health readiness assessment tools for public and private healthcare institutions in developing countries, and to test these tools in Pakistan. Tools were developed using participatory action research to capture partners' opinions, reviewing existing tools, and developing a conceptual framework based on available literature on the determinants of access to e-health. Separate tools were developed for managers and for healthcare providers to assess e-health readiness within their institutions. The tools for managers and healthcare providers contained 54 and 50 items, respectively. Each tool contained four categories of readiness. The items in each category were distributed into sections, which either represented a determinant of access to e-health, or an important aspect of planning. The conceptual framework, and the validity and reliability testing of these tools are presented in separate papers. e-Health readiness assessment tools for healthcare providers and managers have been developed for healthcare institutions in developing countries.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".