Validation of an e-health readiness assessment framework for developing countries
Bibliographic record
Abstract
BACKGROUND: Studies document e-health as having potential to improve quality of healthcare services, resulting in both developed and developing countries demonstrating continued interest in e-health uptake and use. e-Health implementations are not always successful as high failure rates have been reported in both developed and developing countries. These failures are often a result of lack of e-health readiness. e-Health readiness has been defined as the preparedness of healthcare institutions or communities for the anticipated change brought by programs related to information and communication technologies. As such it is critical to conduct an e-health readiness assessment prior to implementation of e-health innovations so as to reduce chances of project failure. Noting the absence of an adequate e-health readiness assessment framework (eHRAF) suitable for use in developing countries, the authors conceptualised, designed, and created a developing country specific eHRAF to aid in e-health policy planning. The aim of this study was to validate the developed eHRAF and to determine if it required further refinement before empirical testing. METHODS: Published options for a framework validation process were adopted, and fifteen globally located e-health experts engaged. Botswana experts were engaged using saturation sampling, while international experts were purposively selected. Responses were collated in an Excel spreadsheet, and NVivo 11 software used to aid thematic analysis of the open ended questions. RESULTS: Analysis of responses showed overall support for the content and format of the proposed eHRAF. Equivocal responses to some open ended questions were recorded, most of which suggested modifications to terms within the framework. One expert from the developed world had alternate views. CONCLUSIONS: The proposed eHRAF provides guidance for e-health policy development and planning by identifying, in an evidence based manner, the major areas to be considered when preparing for an e-health readiness assessment in the context of 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 | 0.117 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".