Development of an evidence-based e-health readiness assessment framework for Uganda
Bibliographic record
Abstract
BACKGROUND: While e-health readiness assessment is vital to the successful implementation of e-health innovations, there is little published guidance (i.e. e-health readiness assessment frameworks (eHRAFs)) for institutions and countries. OBJECTIVE: To develop an evidence-based and locally relevant eHRAF for Uganda. METHOD: A list of possible e-health readiness domains and constructs was developed through a structured review of the e-health literature. This list was first refined using author experience, insight and reflection. Based on this refined list, an eHRAF questionnaire was developed, which was initially pilot tested for face and content validity. Thereafter, it was distributed to 13 purposively selected study participants who were Ugandan e-health experts from the fields of health, information and communications technology (ICT) and academia. The questionnaire was discussed in a focus group setting for consensus input, where study participants confirmed, rejected or revised proposed domains and constructs suitable to guide e-health readiness assessment at either the national or site-specific level within Uganda. RESULTS: Of 148 identified literature resources, 13 met inclusion criteria. A subjective review highlighted 11 frequently used e-health domains. Further reflection reduced these to nine domains, which were shared with study participants by means of the questionnaire. Based upon prior use of, and familiarity with, a management tool (PESTEL), participants' consensus on factors essential for readiness assessment in Uganda was aligned with PESTEL's six domains: political, economic, sociocultural, technological, environmental, and legal and regulatory. The participants considered engagement, and core and societal readiness as optional domains. Based on this input, the authors developed a proposed eHRAF suitable for Uganda, comprised of domains, sub-domains and constructs. CONCLUSION: The eHRAF developed in this research is an evidence-based framework (literature and cross-sectoral expert opinion) and consists of primary domains, sub-domains and constructs suitable for assessing e-health readiness in Uganda, either nationally or locally, prior to implementation of any e-health system. The process and principles may have utility in other countries. IMPLICATIONS: A national, culturally relevant, context-specific Ugandan eHRAF could facilitate efficient and effective planning and implementation of new e-health programmes across the country and assist policymakers and legislators to develop consistent and reliable guidelines and regulations.
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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 | 0.108 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| 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".