Applicability of the five case model to African eHealth investment decisions
Bibliographic record
Abstract
BACKGROUND: eHealth programmes in African countries face fierce competition for scarce resources. Such initiatives should not proceed without adequate appraisal of their probable impacts, thereby acknowledging their opportunity costs and the need for appraisals to promote optimal use of available resources. However, since there is no broadly accepted eHealth impact appraisal framework available to provide guidance, and local expertise is limited, African health ministries have difficulty completing such appraisals. The Five Case Model, used in several countries outside Africa, has the potential to function as a decision-making tool in African eHealth environments and serve as a key component of an eHealth impact model for Africa. METHODS: This study identifies internationally recognised metrics and readily accessible data sources to assess the applicability of the model's five cases to African countries. RESULTS: Ten metrics are identified that align with the Five Case Model's five cases, including nine component metrics and one summary metric that aggregates the nine. The metrics cover the eHealth environment, human capital and governance, technology development, and finance and economics. Fifty-four African countries are scored for each metric. Visualisation of the metric scores using spider charts reveals profiles of the countries' relative performance and provides an eHealth Investment Readiness Assessment Tool. CONCLUSION: The utility of these comparisons to strengthen eHealth investment planning suggests that the five cases are applicable to African countries' eHealth investment decisions. The potential for the Five Case Model to have a role in an eHealth impact appraisal framework for Africa should be validated through field testing.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".