Appraising eHealth Investment for Africa: Scoping Review and Development of a Framework
Bibliographic record
Abstract
BACKGROUND: As opportunities grow for resource-constrained countries to use eHealth (digital health) to strengthen health systems, a dilemma arises. Wise eHealth investments require adequate appraisal to address opportunity costs. Economic appraisal techniques conventionally utilised for this purpose require sufficient economic expertise and adequate data that are frequently in short supply in low- and middle-income countries. This paper aims to identify, and, if required, develop, a suitable framework for performing eHealth investment appraisals in settings of limited economic expertise and data. METHODS: Four progressive steps were followed: (1) identify required framework attributes from published checklists; (2) select, review, and chart relevant frameworks using a scoping review; (3) analyse the frameworks using deductive and inductive iterations; and, if necessary, (4) develop a new framework using findings from the first three steps. RESULTS: Twenty-four candidate investment appraisal attributes were identified and seven relevant frameworks were selected for review. Analysis of these frameworks led to the refinement of the candidate attributes to 23 final attributes, and each framework was compared against them. No individual framework adequately addressed sufficient attributes. A new framework was developed that addressed all 23 final attributes. CONCLUSIONS: A new evidence-based investment appraisal framework has been developed that provides a practical, business case focus for use in resource-constrained African settings.
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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 | 0.215 | 0.321 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.050 | 0.036 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| 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".