A New eHealth Investment Appraisal Framework for Africa: Validation
Bibliographic record
Abstract
(1) Background: Decisions to use eHealth are complex and involve addressing a large opportunity cost. Sound choices are essential. Weighing up investment options is challenging in resource-constrained settings where there are frequently insufficient economics data and expertise to conduct adequate appraisals. To address this, a new eHealth Investment Appraisal Framework (eHIAF) for Africa has been designed and developed. The aim of this paper was to validate the new framework to consider whether it is fit for purpose and to refine it as needed. (2) Methods: An online survey of purposively selected eHealth experts was used to conduct a desktop validation of the proposed eHIAF for Africa. The survey covered the framework development process, structure, content, completeness, and utility. Expert opinions were charted, and a reflective and iterative process used to assess the tool and extract recommendations for refinement. (3) Results: Eleven eHealth experts who completed the survey had experience in African countries and elsewhere. The majority agreed with the eHIAF for Africa development approach and output. They provided valuable suggestions for minor refinements and felt that with these amendments, the eHIAF for Africa would be 'fit for purpose'. (4) Conclusions: The eHIAF for Africa is considered appropriate for use by policy- and decision-makers working in resource-constrained settings who face the task of selecting optimal eHealth investments. It has the potential for applicability beyond Africa and the framework should now be tested in African 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 | 0.199 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".